S S symmetry

Review Research Review on Space Detection Method

Yong Ma 1,*, Yangguo Liu 1 , Lin Zhang 2 , Yuanlong Cao 3 , Shihui Guo 4 and Hanxi Li 1

1 School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China; [email protected] (Y.L.); [email protected] (H.L.) 2 School of Software, Tongji University, Shanghai 201804, China; [email protected] 3 School of Software, Jiangxi Normal University, Nanchang 330022, China; [email protected] 4 School of Informatics, Xiamen University, Xiamen 361005, China; [email protected] * Correspondence: [email protected]

Abstract: The parking assist system is an essential application of the car’s active collision avoidance system in low-speed and complex urban environments, which has been a hot research topic in recent years. Parking space detection is an important step of the parking assistance system, and its research object is parking spaces with symmetrical structures in parking lots. By analyzing and investigating parking space information measured by the sensors, reliable detection of sufficient parking spaces can be realized. First, this article discusses the main problems in the process of detecting parking spaces, illustrating the research significance and current research status of parking space detection methods. In addition, it further introduces some parking space detection methods, including free-space-based methods, parking-space-marking-based methods, user-interface-based methods, and infrastructure-based methods, which are all under methods of parking space selection. Lastly, this article summarizes the parking space detection methods, which gives a clear direction for future research.

Keywords: parking assist system; parking space detection; symmetrical structures; sensors; reliable detection; free space; parking space marking; user interface; infrastructures  

Citation: Ma, Y.; Liu, Y.; Zhang, L.; Cao, Y.; Guo, S.; Li, H. Research 1. Introduction Review on Parking Space Detection With the rapid economic and social development, the number of motor vehicles in Method. Symmetry 2021, 13, 128. https://doi.org/10.3390/ China has increased rapidly. In contrast, the construction of parking lots has been relatively sym13010128 slow, and the problem of parking difficulties has become increasingly prominent. Research on parking space detection methods can not only effectively increase the utilization rate of Received: 1 December 2020 parking spaces but also alleviate the problem of limited parking space resources, and meet Accepted: 28 December 2020 the requirements of the parking lot, including efficiency, safety, and management [1]. Published: 13 January 2021 Currently, there are many detection methods for parking spaces. According to the categories of the selected sensors, they can be divided into visual and non-visual detection Publisher’s Note: MDPI stays neu- methods [2]. The vision-based detection methods mainly use monocular cameras, binocular tral with regard to jurisdictional clai- cameras, or RGB-Depth (RGB-D) cameras. By using cameras for image acquisition, based ms in published maps and institutio- on the captured images, algorithms are applied to detect parking spaces. nal affiliations. Those approaches provide real-time visual assistance and rich image information, but it could be greatly affected by environmental factors. They are also usually computationally expensive, and it performs poorly in a dark environment. In contrast, non-visual detection methods mainly use ultrasonic sensors, short-range , or laser scanners. In this way, Copyright: © 2021 by the authors. Li- censee MDPI, Basel, Switzerland. they send microwave signals to the surroundings and determine the distance from the This article is an open access article sensor itself to the environment based on the “time of flight” (TOF). This kind of microwave distributed under the terms and con- sensor is independent of the lighting conditions in the close range. Data processing is ditions of the Creative Commons At- straightforward and, therefore, fast, usually in a real-time speed. Therefore, in most tribution (CC BY) license (https:// scenarios, it can meet the requirements of parking measurement in terms of measuring creativecommons.org/licenses/by/ distance and accuracy, but it also has some shortcomings, such as a large beam angle, poor 4.0/). direction, low resolution, short working distance, and other shortcomings.

Symmetry 2021, 13, 128. https://doi.org/10.3390/sym13010128 https://www.mdpi.com/journal/symmetry Symmetry 2021, 13, 128 2 of 18

Table1 summarizes the advantages and disadvantages of different sensors.

Table 1. Advantages and disadvantages of various sensors.

Sensor Type Advantages Disadvantages

Symmetry 2021, 13, x FOR PEER REVIEW • Low2 of 19 accuracy • Low cost Ultrasonic radars and • Short-range • Long life short-range radars • Cannot be used in vertical park- • Small size most scenarios, it can meet the requirements of parking measurement in terms ofing meas- mode uring distance and accuracy, but it also has some shortcomings, such as a large beam an- gle, poor direction, low resolution, short working distance, and other shortcomings. Table 1 summarizes the advantages and disadvantages of different sensors. • High cost • High stability • Short life Laser scannersTable 1. Advantages and disadvantages of various sensors. • High precision • Easy to be affected by rain and Sensor Type Advantages Disadvantages snow • Low accuracy Ultrasonic radars • Low cost • Short-range and short-range • Long life • • Cannot be used in vertical parking radars • Small size Low cost mode • Long life • Weak robustness Vision sensors • High cost • High stability• High accuracy Laser scanners • Short life • High precision• Rich image information • Easy to be affected by rain and snow • Low cost • Long life Vision sensors • Weak robustness After collating• andHigh accuracy summarizing the relevant literature, this paper subdivides the parking space detection• Rich image methods, information which are vision-based and non-visual-based parking spaceAfter detection collating and methods. summarizing Vision-based the relevant literature, parking this detectionpaper subdivides methods the can be further divided parking space detection methods, which are vision-based and non-visual-based parking intospace detection parking methods. space Vision-based markings parkin andg detection user interface-based methods can be further methods. divided Moreover, non-visual- basedinto parking parking space markings space detectionand user inte isrface-based divided methods. into free-space-based Moreover, non-visual- and infrastructure-based methods.based parking Accordingspace detection to is divided the reasoning into free-space-based process and of infrastructure-based effective parking spaces, each method methods. According to the reasoning process of effective parking spaces, each method containscontains several several sub-methods, sub-methods, as shown in asFigure shown 1. in Figure1.

FigureFigure 1. Classification 1. Classification of parking of space parking detection space methods. detection methods. Section 1 of this article introduces two mainstream parking space detection methods and theSection main problems.1 of this Section article 2 introduces introduces the relevant two background mainstream and early parking research. space detection methods andSections the 3–6 main analyze problems. and summarize Section parking2 introduces space detection the methods relevant based background on free and early research. space, parking space marking, user interface, and infrastructure. Section 7 summarizes the Sectionsfull text and3 –looks6 analyze forward to and future summarize research directions. parking space detection methods based on free space, parking space marking, user interface, and infrastructure. Section7 summarizes the full text and looks forward to future research directions.

2. Related Background and Early Research In recent years, with rapid economic development and increasing improvement of people’s living standards, the per capita ownership of automobiles has also increased year by year. According to the Traffic Management Bureau of the Ministry of Public Security, by the end of 2019, our country’s car ownership has reached 260 million. The number of car drivers has reached 397 million, indicating that China has entered the automobile Symmetry 2021, 13, 128 3 of 18

era. Simultaneously, the increasing growth in the number of cars has also brought about static traffic and other problems. Static traffic refers to the parking of vehicles, including short-term parking due to passengers getting on and off or loading and unloading of goods, and long-term parking in parking lots. The problems of static traffic are mainly manifested in the following aspects: planning and layout, insufficient control of land use indicators, fewer parking spaces in parking lots, fewer parking spaces in public buildings, and a severe road occupation phenomenon. It can be seen from the above situation that the parking problem is particularly serious. Therefore, improving parking space detection technology has become the priority of alleviating parking difficulties. In recent years, with the continuous development of parking space detection technology, researchers have developed various parking space detection methods to help drivers park and enter the garage more quickly with real-time parking space information. With the rapid growth of demand for parking assistance systems in recent years, researchers have proposed a variety of parking space detection methods, which can be divided into fully automatic and semi-automatic detection methods. The fully automatic parking space detection methods refer to there being no need for manual intervention. This detection system automatically selects the required parking space. However, there are great constraints on the types of parking spaces inside because all the detected parking space types are predefined. The system cannot detect that it is not within the defined range. In contrast, the semi-automatic parking space detection method means that, during the parking space detection process, human-computer interaction is required to complete the detection of available parking spaces. Compared with the fully automatic method, the semi-automatic method may produce more reliable results and consume less computing resources because it has additional information from the driver. For example, ’s IPA (Intelligent Parking Assist System) is a typical semi-automatic parking system that displays potential parking spaces on the rear-view camera image through a human-machine interface display and enables the driver to use direction control buttons to change the location of the parking space. Although semi-automatic parking space detection has greatly reduced the driver’s difficulty in parking, people still find it too cumbersome and complicated in daily use. Therefore, researchers have proposed a variety of fully automatic parking space detection methods. Kaempchen et al. [3] developed a stereo-vision-based method, which uses a feature-based stereo algorithm, a template matching algorithm on a depth map, and an iterative closest point algorithm to perform a three-dimensional fitting of a vehicle plane model. Xu et al. [4] proposed a parking space marking method based on Restricted coulomb energy (RCE) color segmentation, contour extraction by the least square method, and inverse perspective transformation theory. However, this system only relies on parking lot markings. It may be degraded due to poor visual conditions such as stains, shadows, and occlusions from adjacent vehicles on the markings. With the continuous development of parking space detection technology, more re- searchers are exploring parking space detection methods based on semi-automatic and fully automatic methods, which promote the continuous progress and development of this field. This article will summarize and classify parking space detection methods and technologies. At the same time, it will analyze and explain the characteristics of current parking space detection technology, concluding the challenges faced by parking space de- tection technology in this field in order to better solve parking spaces for future researchers. All of this can provide a reference or help in detecting problems.

3. Free-Space-Based Methods Free-space-based methods are to realize the detection of available parking spaces by identifying the surrounding environment of adjacent vehicles and analyzing the space structure around the vehicles. Among the commonly used sensors are ultrasonic sensors, laser sensors, stereo cameras, depth cameras, and other cameras. These sensors can perceive the environment around the vehicle and provide reliable reference data for parking space Symmetry 2021, 13, x FOR PEER REVIEW 4 of 19

Symmetry 2021, 13, x FOR PEER REVIEW 4 of 19 Symmetry 2021, 13, x FOR PEER REVIEW 4 of 19

3. Free-Space-Based Methods 3. Free-Space-BasedFree-space-based Methods methods are to realize the detection of available parking spaces by Symmetry 2021, 13, 128 3. Free-Space-Based Methods 4 of 18 identifyingFree-space-based the surrounding methods environment are to realize of the adjacent detection vehicles of available and analyzing parking spacesthe space by identifyingstructureFree-space-based around the surrounding the vehicles. methods environment Among are to realizethe commonly of theadjacent detection used vehicles sensorsof available and are analyzing ultrasonic parking thespaces sensors, space by structurelaseridentifying sensors, around the stereo surrounding the cameras,vehicles. environmentAmongdepth camera the commonly ofs, andadjacent other used vehicles cameras. sensors and Theseare analyzing ultrasonic sensors the cansensors, space per- laserceivestructure sensors,the environmentaround stereo the cameras, vehicles. around depth Amongthe vehicle camera the andcommonlys, andprovide other used reliable cameras. sensors reference These are ultrasonic sensorsdata for canparkingsensors, per- detection. According to the differentceivespacelaser the sensors,detection. discrimination environment stereo According cameras, around methodsto depth thethe vehicle different camera of and ,discrimination provideand other reliable spaces, cameras. methods reference free-space-These of datasensorsparking for parking canspaces, per- spacefree-space-basedceive thedetection. environment Accordingmethods around canto thethebe vehicledifferentfurther and divided discrimination provide into reliable direct-ranging-based methods reference of parkingdata for methods, parkingspaces, based methods can be furtherfree-space-based dividedprobability-maps-basedspace detection. into Accordingmethods direct-ranging-based methods, can to thebe furtheranddifferent 3D-rec divided discriminationonstruction-based methods, into direct-ranging-based methods probability- methods. of parking This methods, sectionspaces, maps-based methods, and 3D-reconstruction-basedprobability-maps-basedwillfree-space-based analyze and introducemethods methods, methods.thecan characteristics be andfurther 3D-rec This divided ofonstruction-based sectionthese into three willdirect-ranging-based methods, analyze methods. as shown andThis inmethods, section Tableprobability-maps-based 2. methods, and 3D-reconstruction-based methods. This section introduce the characteristics of thesewill analyze three and methods, introduce the as characteristics shown in of Table these three2. methods, as shown in Tablewill analyze 2. and introduce the characteristics of these three methods, as shown in Table 2. Table 2. Free-space-based method. Table 2. Free-space-based method.Table 2. Free-space-based method. Methods Characteristics Table 2. Free-space-based method. Method Process Methods Characteristics Method Process MethodsMethods CharacteristicsUsingCharacteristics direct range sensor Sensor data Method Method ProcessAttitude estimation Direct-ranging- Usingto measure direct therange distance sensor Sensor data Attitude estimation Direct-ranging-based methods Usingtoto surroundingmeasure direct the range distanceobstacles sensor Sensor data Attitude estimation UsingbasedDirect-ranging- methods direct range sensor to measure the Parking space Space analysis Direct-ranging-based methods toto surrounding measure the obstacles distance baseddistance methods to surrounding obstacles Parking space Space analysis to surrounding obstacles Parking space Space analysis

Sensor data Sensor data Using probability maps Probability-maps- Sensor data toUsing indicate probability the occupancy maps Probability maps Parking space Probability-maps-based methods Using probability maps Probability-maps-Using probabilitytoof indicatethe maps surrounding the to occupancy indicate space the Probability maps Parking space Probability-maps-based methods based methods to indicate the occupancy basedoccupancy methodsof of the the surrounding surrounding space space Probability maps Parking space of the surrounding space Candidate parking space Classification Candidate parking space Classification Candidate parking space Classification

Sensor data Parking space Reconstructing the 3D Sensor data Parking space Reconstructing3D-reconstruction- theReconstructingenvironment 3D environment from the the3D from Sensor data Parking space 3D-reconstruction-based methods Reconstructing the 3D 3D data Model matching 3D-reconstruction-basedthe methods data measured environmentdata measured by the from by sensors the 3D-reconstruction-based methods dataenvironment measuredsensors from by the the 3D data Model matching 3D data Model matching based methods data measuredsensors by the 3D-reconstruction Space analysis sensors 3D-reconstruction Space analysis 3D-reconstruction Space analysis 3.1. Direct-Ranging-Based Parking Space Detection Methods 3.1. Direct-Ranging-BasedRanging sensors can Parking emit microwave Space Detection signals Methods of a specific frequency and use an air 3.1. Direct-Ranging-Based Parking Space Detection Methods 3.1. Direct-Ranging-Based Parkingmedium SpaceRanging to Detection propagate. sensors can According Methods emit microwave to the reflec signalstion principle of a specific of microwave frequency signals and use encoun- an air Ranging sensors can emitmediumtering microwaveRanging obstacles, to propagate. sensors the signals distance canAccording emit ofbetween microwave to a the specific thereflec sensorsignalstion frequency principle andof a thespecific ofobstacle microwave frequency and can use be signals andcalculated an use encoun- an byair teringmeasuringmedium obstacles, to the propagate. signal the distancepropagation According between totime the thein reflec the sensor tionair. Therefore, principleand the obstacle of installing microwave can a distancebe signals calculated measur-encoun- by air medium to propagate. Accordingmeasuringingtering sensor obstacles, the toon signalthe the the vehicle reflectionpropagationdistance can betweensense time principle the inthe distancethe sensor air. Therefore,of betweenand microwave the obstacle theinstalling vehicle can a signals distance andbe calculated the measur-adjacent by encountering obstacles, the distanceingparkedmeasuring sensor between vehicle. onthe thesignal The vehicle the space propagation sensor canstructure sense and time aroundthe in the distance the the obstacle air. vehicle Therefore,between can can the beinstalling be estimatedvehicle calculated a anddistance based the adjacent onmeasur- these ing sensor on the vehicle can sense the distance between the vehicle and the adjacent by measuring the signal propagationparkedcontinuously vehicle. time measured The in space the data. structure air. Available Therefore, around parking the vehiclespace installing can can be be obtained estimated a distance by basedcomparing on these the continuouslysizeparked of freevehicle. space measured The and space the data. vehicle.structure Available This around parkingmethod the spacevehicleis referred can can be tobe obtained asestimated a direct-ranging-based by comparingbased on these the measuring sensor on the vehiclesizeparkingcontinuously of can free space sensespace measureddetection and the the method.data.distance vehicle. Available This between parkingmethod spaceis referred the can vehicle be to obtained as a direct-ranging-based and by comparing the the adjacent parked vehicle. The spaceparkingsize structureBasedof freespace on space detectionthe around ideaand ofthe method. sensor vehicle. the ranging, vehicle This method Pohl can et al.is be referred[5] estimated designed to as a asemi-automated baseddirect-ranging-based on park- ingparking assistanceBased space on thesystemdetection idea by of method. seekingsensor ranging, to reuse Pohlthe built-in et al. [5] sensors designed of th a esemi-automated vehicle and adding park- a these continuously measured data.ingfew assistance additional AvailableBased on system thecomponents idea parking by of seeking sensor to spacethe ranging,to vehicle. reuse can thPohl Whene be built-in et obtained al.the [5] sensorsvehicle designed byisof running,th a comparinge semi-automated vehicle the and system adding park- con- a the size of free space and the vehicle.fewtinuouslying assistanceadditional This detects system methodcomponents the distanceby seeking is to referred tothe theto vehicle. reuse surrounding toth Whene asbuilt-in athe vehicles direct-ranging-based vehiclesensors through is of running, the thevehicle ultrasonic the and system adding sensor con- a parking space detection method.tinuouslyfew additional detects components the distance to to the the vehicle. surrounding When thevehicles vehicle through is running, the ultrasonic the system sensor con- tinuously detects the distance to the surrounding vehicles through the ultrasonic sensor Based on the idea of sensor ranging, Pohl et al. [5] designed a semi-automated parking assistance system by seeking to reuse the built-in sensors of the vehicle and adding a few

additional components to the vehicle. When the vehicle is running, the system continuously detects the distance to the surrounding vehicles through the ultrasonic sensor to detect whether there is available parking space, which can be seen in Figure2. However, due to the characteristics of a sound wave reflection, only when the sound wave is orthogonal to the reflecting surface can the distance between the two be accurately measured. The reason lies in that the corners of the vehicle are not regular right angles. The ultrasonic sensor has a large error at the corner of the vehicle during the measurement process. In order to solve such problems, Jeong et al. [6] proposed a parallel parking assist system (PPAS) structure with software and hardware cooperation. The system uses a new sensor algorithm to reduce the vehicle angle error of the ultrasonic sensor and can successfully realize parking space detection and automatic control. Due to the cheap basic hardware and simple software design, the implementation of PPAS is possible. Jiang et al. [7] proposed a multi-sensor-based parking space recognition method. Given the shortcomings of existing methods, this method uses lidar to detect the edge points of parking spaces. It also employs the camera to extract the contours of the parking space’s edge. Moreover, it combines the length of the library detected by the lidar with the contours of the parking space edges extracted from the image to determine whether the Symmetry 2021, 13, x FOR PEER REVIEW 5 of 19

to detect whether there is available parking space, which can be seen in Figure 2. How- ever, due to the characteristics of a sound wave reflection, only when the sound wave is Symmetry 2021, 13, 128 orthogonal to the reflecting surface can the distance between the two be accurately meas- 5 of 18 ured. The reason lies in that the corners of the vehicle are not regular right angles. The ultrasonic sensor has a large error at the corner of the vehicle during the measurement process. In order to solve such problems, Jeong et al. [6] proposed a parallel parking assist system (PPAS) structure with software and hardware cooperation. The system uses a new parkingsensor algorithm space to meets reduce the vehicle requirements. angle error of Thisthe ultrasonic method sensor can and further can suc- identify parking spaces in morecessfully complex realize parking scenes space and detection improve and automatic parking steering space control. resources. Due to the cheap basic hardware and simple software design, the implementation of PPAS is possible.

Figure 2. Direct-ranging-based method. Figure 2. Direct-ranging-based method. Jiang et al. [7] proposed a multi-sensor-based parking space recognition method. 3.2.Given Probability-Maps-Based the shortcomings of existing methods, Parking this Space method Detection uses lidar Methods to detect the edge points of parking spaces. It also employs the camera to extract the contours of the parking space’sAt edge. present, Moreover, there it combines are manythe length problems of the library with detected the by direct-ranging-based the lidar with parking space detectionthe contours method.of the parking Therefore, space edges researchersextracted from the use image map to representationdetermine whether methods and mathemati- calthe statisticsparking space to meets divide the therequirements. environment This method into can a seriesfurther identify of small parking grids in which each is given spaces in more complex scenes and improve parking space resources. a possible value to express the probability that the grid is occupied. The parking spaces around3.2. Probability-Maps-Based the vehicle Parking are estimated Space Detection by Methods the degree of grid occupancy. This method is called the probability-maps-basedAt present, there are many problems parking with the space direct-ranging-based detection method. parking space detection method. Therefore, researchers use map representation methods and mathemat- ical statisticsSchmid to divide et al. the [ 8environment] proposed into a a parkingseries of small space grids detectionin which each method is given based on a hierarchical three-dimensionala possible value to express occupancy the probability grid, that the which grid is usesoccupied. a three-dimensional The parking spaces occupancy grid based around the vehicle are estimated by the degree of grid occupancy. This method is called onthe anprobability-maps-based octree hierarchical parking data space structure detection method. to represent the surrounding environment. It can be seenSchmid in et Figure al. [8] proposed3. The a detailed parking space level detection of the method dynamic based on control a hierarchical grid divides the intersection betweenthree-dimensional the detected occupancy grid, curb which boundary uses a three-dimensional and the vehicle occupancy boundary grid based into smaller sections. In on an octree hierarchical data structure to represent the surrounding environment. It can addition,be seen in Figure it determines 3. The detailed thelevel sizeof the dynamic of the parkingcontrol grid spacedivides the by intersection analyzing the maximum vertical distancebetween the between detected curb the boundary vehicle and boundary the vehicle setboundary and theinto smaller intersection. sections. In It is widely acknowledged thataddition, detecting it determines free the parking size of the spaces parking inspace a two-dimensionalby analyzing the maximum dense vertical grid is easy to operate. This distance between the vehicle boundary set and the intersection. It is widely acknowledged methodthat detecting solves free parking the problem spaces in a oftwo-dimensional detectingfree dense parking grid is easy spaces to operate. in This a layered three-dimensional datamethod structure. solves the problem Furthermore, of detecting using free parking a layered spaces datain a layered structure three-dimen- dramatically reduces the cost Symmetry 2021, 13, x FOR PEER REVIEWsional data structure. Furthermore, using a layered data structure dramatically reduces6 of 19

ofthethree-dimensional cost of three-dimensional spacespace representation. representation.

Figure 3. Occupying grid map. Figure 3. Occupying grid map. Dube et al. [9] borrowed technology from the field of computer vision to extract park- ing spacesDube from et the al. occupancy [9] borrowed grid. According technology to Reference from [10], thea -based field of occupancy computer vision to extract park- inggrid spaces was established from theutilizing occupancy extracting grid.parking According candidate vehicles to Reference from this grid [10 and], a radar-based occupancy describing and classifying these candidates to determine whether there are vehicles in the gridgrid. This was method established solves the problem utilizing of detecting extracting parallel and parking vertical parking candidate at the same vehicles from this grid and describingtime. Loeffler et and al. [11] classifying used an occupancy these grid candidates based on the toDempster-Shafer determine theory whether [12]. there are vehicles in the This method accumulates the measured static cluster information in the grid, and two grid.compensation This method factor (Gaussian solves factor the and problem saturation of factor) detecting coefficients parallel are introduced and vertical to parking at the same time.improve Loeffler the accuracy et al.of parking [11] used space detection. an occupancy This method grid may based detect parking on the spaces Dempster-Shafer theory [12]. Thison the method other side of accumulates the road, even at the higher measured vehicle speeds. static Prophet cluster et al. [13] information designed in the grid, and two an impressive two-dimensional vector target list, which uses a special contour filter to sort compensationall targets in the environmental factor (Gaussian map, detect factorparallel parking and saturation spaces and vertical factor) parking coefficients are introduced tospaces, improve and park the them. accuracy Perform a qualitative of parking assessment. space Compared detection. with the This commonly method may detect parking used grid map method, this method processes radar data in a specially designed two- spacesdimensional on vector the othertarget list. side Therefore, of the quantization road, even errors are at avoided, higher and vehicle the compu- speeds. Prophet et al. [13] designedtational burden an is impressive reduced. two-dimensional vector target list, which uses a special contour filterAndre to sort et al. all [14] targets came up in with the a environmentalnew parking garage map, vehicle detect location paralleland tracking parking spaces and vertical system with environment-based embedded lidar sensors. The method integrates data from multiple sensors, allowing vehicles to be tracked in a public parking lot coordinate system. Simultaneously, a combination of the grid-based method, the random sample con- sensus (RANSAC) algorithm, and the Kalman filter is used to realize real-time vehicle detection and tracking, and highly confident and accurate vehicle positioning can be achieved. Scheunert et al. [15] developed a method for parking space detection, which uses a polarization mode dispersion (PMD) three-dimensional range-finder camera. The sensor allows reference to a large number of spatial point measurements, representing the cut of the observed scene in detail. The feature extraction is based on the feature consistency of the occupied grid and the detection channel. This method focuses on the feature extraction of PMD data and the feature fusion that defines parking spaces’ free space.

3.3. 3D-Reconstruction-Based Parking Space Detection Methods 3D-reconstruction-based parking space detection method realizes the detection of the parking space by reconstructing the three-dimensional space model around the vehicle. This method intuitively displays the space structure around the vehicle and is easy for the driver to understand. Park et al. [16] proposed combining ultrasonic sensors and three-dimensional vision sensors to detect parking spaces. The shape of the parked vehicle is modeled by two ver- tical planes: a longitudinal plane and a transverse plane. The ultrasonic data calculate the former, and the three-dimensional vision data obtains the lateral plane. Moreover, the

Symmetry 2021, 13, 128 6 of 18

parking spaces, and park them. Perform a qualitative assessment. Compared with the commonly used grid map method, this method processes radar data in a specially designed two-dimensional vector target list. Therefore, quantization errors are avoided, and the computational burden is reduced. Andre et al. [14] came up with a new parking garage vehicle location and tracking system with environment-based embedded lidar sensors. The method integrates data from multiple sensors, allowing vehicles to be tracked in a public parking lot coordinate system. Simultaneously, a combination of the grid-based method, the random sample consensus (RANSAC) algorithm, and the Kalman filter is used to realize real-time vehicle detection and tracking, and highly confident and accurate vehicle positioning can be achieved. Scheunert et al. [15] developed a method for parking space detection, which uses a polarization mode dispersion (PMD) three-dimensional range-finder camera. The sensor allows reference to a large number of spatial point measurements, representing the cut of the observed scene in detail. The feature extraction is based on the feature consistency of the occupied grid and the detection channel. This method focuses on the feature extraction of PMD data and the feature fusion that defines parking spaces’ free space.

3.3. 3D-Reconstruction-Based Parking Space Detection Methods 3D-reconstruction-based parking space detection method realizes the detection of the parking space by reconstructing the three-dimensional space model around the vehicle. This method intuitively displays the space structure around the vehicle and is easy for the driver to understand. Park et al. [16] proposed combining ultrasonic sensors and three-dimensional vision sensors to detect parking spaces. The shape of the parked vehicle is modeled by two vertical planes: a longitudinal plane and a transverse plane. The ultrasonic data calculate the former, and the three-dimensional vision data obtains the lateral plane. Moreover, the empty spaces between adjacent vehicles are the detected parking spaces. This method uses a vision sensor to make up for the inaccurate measurement of the ultrasonic at the corner of the vehicle. In order to identify parking spaces, Zhou et al. [17] proposed a supervised learning method by detecting parked vehicles and explaining the parking environment. In this way, vehicle detection can be achieved by identifying vehicle bumpers from laser range scans. Among them, AdaBoost [18] is used to train a classifier based on the relevant geometric features of the data segment corresponding to the car bumper. In this method, the detected bumper is used as the symbol of the vehicle hypothesis. A topology map representing the structure of the parking space is constructed, and then the topology map is spatially analyzed to identify potential parking spaces. Lee et al. [19] preprocessed the three-dimensional point cloud data and then calculated the minimum size of the parking space based on the vehicle dynamics theory. Kaempchen et al. [3] also started from a set of three-dimensional point cloud data, using a vision-based detection method to determine the position of the three-dimensional point by estimating its position and then extracting potential vehicles from the depth map through template matching. The three-dimensional point cloud data is segmented. Finally, the plane of the vehicle model is fitted to it through the iterative closest point (IPC) algorithm to make the detected vehicle accurate and reliable. Unger et al. [20] identified an efficient real-time motion stereo algorithm, allowing for a dense depth map of the vehicle’s observation lateral space on a mobile central processing unit (CPU). Due to the movement of the vehicle, images are acquired in consecutive time instances (that is, from different perspectives) to calculate the disparity map [21]. In order to eliminate abnormal points and improve the accuracy of depth information, the algorithm incorporates the time history of the disparity map. It extracts the ground and obstacles from these measurements, updating the environmental map accordingly. Jung et al. [22] designed a target position method based on light belt projection, which provides an economical target location design method for indoor parking spaces with insufficient light without reducing the car’s interior. This method can add a low-cost light Symmetry 2021, 13, 128 7 of 18

flat projector to the existing rear parking camera. The light streak feature is detected by the difference between the images of the light flat projector turned on and off. The light stripe projection theory is used to reconstruct the three-dimensional information of the parking lot. By normalizing the direction and constructing a depth map, this method can detect free space and select the closest point as the target position. Moreover, it can successfully determine the target position when the light conditions are poor and the reflective surface of adjacent parked vehicles is black. Pelaez et al. [23] explored a method to assist the driver in parking by processing the data acquired by the three-dimensional time-of-flight (ToF) camera and reconstructing objects around the vehicle. This technology mainly focuses on the fusion of two parallel processing technologies. One is the visual processing of intensity images, and the other is the spatial processing of point clouds. Both are centered on detecting the license plate to estimate the position of the license plate and determine the free position in the parking lot. This method solves the problems of performance degradation under bright ambient light (mainly occurring in outdoor parking lots), resulting in shadows and brightness in the image, and limited detection of low-reflective objects, such as dark cars. Meng et al. [24] designed a machine-vision-based parking space detection method. This method first customizes the parking space area, and sampling points are automatically generated in the customized area based on specific rules. In this way, it can greatly reduce the computing resources and storage resources of image processing. Second, parking space detection is carried out according to the change of the gray value of the relevant sampling points when the vehicle enters the parking space. Finally, the virtual parking space is customized by the height of the space, which solves the problem of vehicle occlusion in adjacent parking spaces. Various interferences such as strange objects on the road through shape matching and other algorithms are eliminated. In the process of traditional 3D reconstruction, there are a large number of false 3D points, and the density of 3D points has a linear relationship with the number of detected features, which cannot be controlled. In References [25,26], it is mentioned that the detection accuracy of parking spaces can be promoted by improving the tracking efficiency of 3D points, the tracking density, and the quality of the 3D prints of the reconstructed obstacle model.

3.4. Summary At present, the method of detecting parking spaces based on free space has become the mainstream method, and this method only needs to install cheap ranging sensors on the vehicle. At the same time, this method also has certain shortcomings. The detection performance of this method is completely dependent on the adjacent parked vehicles. When there are no parked vehicles around, this method will fail.

4. Parking-Space-Marking-Based Methods Free-space-based parking space detection methods are the most widely used methods, but their performance depends on the pose of neighboring vehicles and the measurement accuracy of the sensors used. Therefore, without adjacent vehicles, free-space-based parking space detection methods cannot work normally and cannot meet the driver’s demand for parking space detection. Therefore, researchers proposed a parking space detection method based on parking space markings, using computer vision technology to determine the location of the parking space by identifying the parking space markings from the image. According to the different detection technologies, it can be divided into three categories: straight-line-detection-based methods, corner detection-based methods, and learning-based detection methods. This section will analyze and introduce these three detection methods, as shown in Table3. Symmetry 2021, 13, x FOR PEER REVIEW 8 of 19

Symmetry 2021, 13, x FOR PEER REVIEW 8 of 19

In the process of traditional 3D reconstruction, there are a large number of false 3D points,In andthe processthe density of traditional of 3D points 3D has reconstruction, a linear relationship there are with a large the number ofof detectedfalse 3D features,points, and which the densitycannot beof 3Dcontrolled. points has In Referencesa linear relationship [25,26], it withis mentioned the number that of the detected detec- tionfeatures, accuracy which of cannot parking be spaces controlled. can be In promoted References by [25,26], improving it is mentioned the tracking that efficiency the detec- of 3Dtion points, accuracy the of tracking parking density, spaces canand bethe promoted quality of by the improving 3D prints the of thetracking reconstructed efficiency ob- of stacle3D points, model. the tracking density, and the quality of the 3D prints of the reconstructed ob- stacle model. 3.4. Summary 3.4. SummaryAt present, the method of detecting parking spaces based on free space has become the mainstreamAt present, method,the method and of this detecting method parking only needs spaces to installbased cheapon free ranging space has sensors become on the mainstreamvehicle. At the method, same time,and this this method method only also needs has certain to install shortcomings. cheap ranging The sensors detection on performancethe vehicle. At of the this same method time, is this completely method alsodependent has certain on the shortcomings. adjacent parked The detectionvehicles. Whenperformance there are of nothis parked method vehicles is completely around, thisdependent method on will the fail. adjacent parked vehicles. When there are no parked vehicles around, this method will fail. 4. Parking-Space-Marking-Based Methods 4. Parking-Space-Marking-BasedFree-space-based parking space Methods detection methods are the most widely used meth- ods, butFree-space-based their performance parking depends space on detection the pose methodsof neighboring are the vehicles most widely and the used measure- meth- mentods, but accuracy their performance of the sensors depends used. Therefore, on the pose without of neighboring adjacent vehicles,vehicles and free-space-based the measure- parkingment accuracy space detectionof the sensors methods used. cannot Therefore, work without normally adjacent and cannot vehicles, meet free-space-based the driver’s de- mandparking for space parking detection space methodsdetection. cannot Therefore, work researchersnormally and proposed cannot meeta parking the driver’s space de- tectionmand for method parking based space on detection.parking space Therefore, markings, researchers using computer proposed vision a parking technology space de- to determinetection method the location based on of parkingthe parking space space markings, by identifying using computer the parking vision space technology markings to fromdetermine the image. the location According of the to parkingthe different space detection by identifying technologies, the parking it can spacebe divided markings into Symmetry 2021, 13, 128 threefrom thecategories: image. Accordingstraight-line-detection-based to the different detection methods, technologies, corner detection-based it can be divided8 ofmethods, 18 into andthree learning-based categories: straight-line-detection-based detection methods. This section methods, will analyze corner detection-based and introduce these methods, three detectionand learning-based methods, asdetection shown methods.in Table 3. This section will analyze and introduce these three detection methods, as shown in Table 3. Table 3. Parking-space-marking-based method. Table 3. Parking-space-marking-basedTable 3. Parking-space-marking-based method. method. Methods Characteristics Method Process MethodsMethods CharacteristicsCharacteristics Method Method Process Input image Parking space straight-line-based Inferring the parking space by Input image Parking space Linear feature extraction Occupation classification straight-line-basedInferringmethods the parking detectingInferring space straightthe parking by line detecting segmentsspace by straight-line-based methods methods detecting straight line segments Linear feature extraction Occupation classification straight line segments Feature clustering/filtering Candidate parking spaces Feature clustering/filtering Candidate parking spaces

Input image Parking space By detecting corner points, infer Input image Parking space corner detection- SymmetryBy detecting 2021, 13, x FOR corner PEERBythe detectingREVIEW points,parking space infercorner based thepoints,parking on infer the Corner detection Occupation classification 9 of 19 corner detection-based methods cornerbased detection-methods space based on thethe cornercorner parking point point space combination combinationbased on the based methods Corner detection Occupation classification corner point combination Node type Parking space type Node type Parking space type

Input image

By extracting the features of the Neural network Feature extraction Bylearning-based extracting themarked features points, of the the markedparking learning-based detection methods detectionpoints, methods the parking space space is determined is determined according according to theto type the type of the of the marked marked pointspoints Color segmentation mark point detection

Parking space

4.1. Straight-Line-Based Parking Space Detection Methods Parking space markings refer to parking spaces composed of guidelines and dividing 4.1. Straight-Line-Based Parkinglines Space attached Detection to the ground Methods in a specific parking lot. There are many types, such as rec- Parking space markingstangular refer to parking parking spaces, spaces parallelogram composed parking of spaces, guidelines and diamond and dividingparking spaces. Since parking space markings are composed of straight-line segments, the detection of lines attached to the groundstraight in a lines specific is essential parking in finding lot. parking There spaces. are many types, such as rectangular parking spaces, parallelogramJung et al. [27] used parking a method spaces, based on and the diamondcombination of parking pixel classification spaces. and Since parking space markingsfeature are matching composed to extract of the straight-line spatial structure segments, information of the the parking detection lot. The of park- ing lot marking is separated by plane constraints and transformed into a bird’s-eye view, straight lines is essential in findingand template parking matching spaces. is performed on the bird’s-eye view to determine the location of Jung et al. [27] used a methodthe parking based lot. The onobstacle the depth combination map generated of by pixel the parallax classification of adjacent vehicles and can be used as a criterion for template matching by limiting the search range and direction. feature matching to extract theThis spatial method structure utilizes the informationobstacle depth map of theand the parking bird’s-eye lot. view The of the parking parking lot lot marking is separated by planemarkings, constraints which effectively and limits transformed the search range into and a improves bird’s-eye the operation view, speed and and template matching is performedrobustness on to the visual bird’s-eye noise. view to determine the location of the Since camera images are not robust for environmental conditions (such as night and parking lot. The obstacle depthbacklight), map Kamiyama generated et al. [28] by came the up parallax with a method of adjacent to identify parking vehicles spaces can so that be used as a criterion for templatethe parking matching spaces can be by used limiting during the the day searchor night, regardless range and of whether direction. the road is This method utilizes the obstacledry or wet. depth This method map anduses the the statistical bird’s-eye model of view the received of the light parking intensity value lot of the laser rangefinder to classify the road surface. Then use Hough transform to estimate markings, which effectively limitsthe target the parking search position. range Jung and et al.improves [27] proposed the a parking operation space marking speed recognition and robustness to visual noise. algorithm based on a monocular vision for automatic selection of the automatic parking assistance system. A one-dimensional filter is designed in the Hough space, using prior Since camera images areknowledge not robust about for the feature environmental of the marking conditions line in the bird’s-eye (such view as nightof the edge and image backlight), Kamiyama et al. [28and] using came the up improved with adistance method between to identify the point and parking the line spacessegment to so distinguish that the parking spaces can be usedthe identified during line the segment. day or The night, algorithm regardless can successfully of whether identify the parking road isspace, even if the adjacent vehicles block the parking space marking line seriously. dry or wet. This method uses theHough statistical transform model is often used of the to detect received the markings light of intensity parking spaces. value However, of the laser rangefinder to classifywhen the Hough road transform surface. detects Then parallel use line Hough pairs, its performance transform is tonot estimate robust because of the influence of factors such as noise, clutter, illumination, and changes in weather con- the target parking position. Jungditions. et Moreover, al. [27] the proposed Hough transform a parking cannot space detect multiple marking parallel recognition line pairs at the algorithm based on a monocularsame time. vision Wang for et al. automatic [29] used a polynomial selection fisheye of the distortion automatic model parkingto calibrate the assistance system. A one-dimensionalcamera. They employed filter is an designed image stitching in the method Hough based space,on the Levenberg-Marquardt using prior algorithm to stitch four images of the fisheye camera into an omni-directional bird’s-eye knowledge about the featureview of the image. marking Second, the line method inthe based bird’s-eye on the Radon view transform of theis used edge to extract image parking and using the improved distancespace betweenfeatures, which the can point detect and multiple the parking line segment spaces at the to same distinguish time. the identified line segment. The algorithmWhen the candetection successfully distance of the identify ultrasonic thesensor parking is less than space, 3 m, the evenresult cannot if be used to extract further information for identifying obstacles. Moreover, systems based the adjacent vehicles block theon parkingrear-view cameras space cannot marking assist in line situations, seriously. such as parallel parking that requires a Hough transform is oftenwider used field toof view. detect the markings of parking spaces. However, when Hough transform detects parallel line pairs, its performance is not robust because of the influence of factors such as noise, clutter, illumination, and changes in weather conditions. Moreover, the Hough transform cannot detect multiple parallel line pairs at the same time. Wang et al. [29] used a polynomial fisheye distortion model to calibrate the Symmetry 2021, 13, 128 9 of 18

camera. They employed an image stitching method based on the Levenberg-Marquardt algorithm to stitch four images of the fisheye camera into an omni-directional bird’s-eye view image. Second, the method based on the Radon transform is used to extract parking space features, which can detect multiple parking spaces at the same time. When the detection distance of the ultrasonic sensor is less than 3 m, the result cannot be used to extract further information for identifying obstacles. Moreover, systems based on rear-view cameras cannot assist in situations, such as parallel parking that requires a wider field of view. Hamada et al. [30] put forward a parking space detection algorithm based on panoramic images. This algorithm solves the tracking problem when the detected parking space falls off the surrounding field of view. Lee et al. [31] designed a tapered hat marking line extraction filter and an entropy-based line marking a clustering algorithm for accurate parking line detection. Even in the case of reduced visibility, the conical cap filter can extract line features powerfully. Then, the entropy-based line marker clustering algorithm quickly and effectively assigns the extracted line markers to each parking line segment. Compared with other linear detectors, including Hough transform, this method is faster and has stronger robustness in the case of distorted or blurred parking lines. In addition, when compared with other methods based on corner detection, the detected parking line provides a more accurate estimation of the target position, direction angle, and occlusion angle. However, changes in light intensity and complex obstacle conditions will seriously affect the performance of the parking space detection system. In addition, the existing parking space detection methods only consider whether large objects occupy the parking space, and ignore the existence of small objects in the parking space. In order to overcome these shortcomings, Li et al. [32] came up with a vision-based automatic parking space detection method that uses a vehicle peripheral vision monitoring system (AVM) composed of four fisheye cameras to detect various parking space signs. This method utilizes a linear segment detector (LSD) based on edge information to detect parking space markings with a pair of parallel lines at a fixed distance in the AVM image. It then uses image segmentation algorithms and stereo vision algorithms to calculate small obstacles in the parking space marking height. This method is superior to the method based on the Hough transform in terms of continuity and completeness. In addition, this method can effectively reduce the frequency of false detections and missed detections. Wenhao et al. [33] came up with an improved LSD detection algorithm, which is divided into the detection thread and tracking thread. In the detection thread, an improved line extractor based on the line segment detector (LSD) [34] obtains the available parking space line edges at the beginning. Second, the parking space angle extractor obtains the structural information of the L-shaped component, which meets the specification requirements of most parking lots. Third, using the L-shaped results detected in the current frame and the L-shaped results obtained from the previous frame tracking, a search method is proposed to obtain candidate parking areas. In the tracking process, an algorithm based on vehicles and Kalman filtering is used to update the actual location of each parking lot and give a confidence score. Finally, with the help of ultrasound and reconfirmation schemes, most of the false alarms are removed to obtain the final detection results, including un-analyzable areas. Li et al. [35] put forward a method based on geometric features, which can obtain better results even when parking space markings are displayed in a fully automatic manner under various lights (dimmed and strong) and ground conditions (brick, curved, blurred, and marking). Based on the original line segment detector (LSD) algorithm, this method first uses line clustering to detect separation lines. Then, according to the geometric characteristics of the parking space, the separation lines are paired to form parking space candidates. Finally, the entrance is detected using straight-line and learning-based methods. Li et al. [36] illustrated an automatic parking space detection and recognition system, which mainly solves the problem of incomplete parking space marking lines and shadows or obstacles in the existing technology, leading to the problem of inaccurate parking space detection and recognition. This method includes the camera detection module and the Symmetry 2021, 13, 128 10 of 18

recognition module, and the camera is installed around the vehicle and stitched into a seamless panoramic image that can reflect the information around the vehicle. The module performs the detection of the parking space marking line and conducts the incomplete parking space detection through the marking line circumference. Then, the recognition module completes the parking space recognition under the shadow or obstacle in the parking space by calculating the difference value of the internal gray change of the parking space and the height of the obstacle. Jae et al. [37] created a method that can be fully automated, identifying various types of parking space markings, and is robust for various lighting conditions. The method first extracts parallel line pairs from the around view monitor (AVM) image to detect the separation line of parking space markings. Then, the separation line detected in the current Symmetry 2021, 13, x FOR PEER REVIEW 11 of 19 image is combined with the separation line detected in the previous image. According to the geometric constraints of the parking space, a parking space candidate is generated

by combiningJae et al. [37] created the a pairing method that of can the be fully dividing automated, lines. identifying These various candidates types were confirmed by identifyingof parking space their markings, entrance and is robust locations for various using lighting line conditions. and cornerThe method features first and using ultrasonic sensorsextracts parallel to classify line pairs their from the occupancy. around view Finally,monitor (AVM) the freeimage parking to detect the space sep- identified in the current aration line of parking space markings. Then, the separation line detected in the current imageimage is iscombined combined with the with separation the free line detected parking in the space previous identified image. According in the previousto image. According tothe the geometric vehicle constraints odometer of the parking based space, on the a parking in-vehicle space candidate motion is sensor,generated theby separation line found in combining the pairing of the dividing lines. These candidates were confirmed by identi- thefying previous their entrance image locations and using the line current and corner position features and of using the parkingultrasonic sensors space are tracked. to classify their occupancy. Finally, the free parking space identified in the current image 4.2.is combined Corner-Based with the free Parking parking Space space identified Detection in the Methods previous image. According to the vehicle odometer based on the in-vehicle motion sensor, the separation line found in the previousMethods imagebased and the oncurrent traditional position of the straight-line parking space detectionare tracked. of parking spaces can only detect one or two types of parking spaces, but there are many types of parking spaces, such as diamonds4.2. Corner-Based or Parking parallelograms. Space Detection TheMethods parking space detection method based on corner features Methods based on traditional straight-line detection of parking spaces can only de- cantect one detect or two a types variety of parking of parking spaces, but space there are types many andtypes of realize parking a spaces, variety such of parking operations. as diamondsSuhr or et parallelograms. al. [38] designed The parking a method space detection for fully method automatic based on corner identification fea- of parking space markings.tures can detect This a variety method of parking models space differenttypes and realize types a variety of parking of parking space opera- markings as a hierarchical tions. treeSuhr structure et al. [38] for designed identification. a method for fully As illustratedautomatic identification in Figure of 4parking, this space method mainly includes two processes:markings. This bottom-up method models and different top-down. types of parking This space method markings gradually as a hierarchical generates candidate parking tree structure for identification. As illustrated in Figure 4, this method mainly includes spacestwo processes: through bottom-up a bottom-up and top-down. process. This method Afterward, gradually according generates tocandidate the nature of the parking space markingparking spaces type, through climbing a bottom-up up theprocess. hierarchical Afterward, according tree structure to the nature from of the top to bottom can eliminate theparking false space parking marking type, spaces, climbing nodes, up the and hierarchical corner tree points. structure Ultimately,from top to bot- the final parking space is tom can eliminate the false parking spaces, nodes, and corner points. Ultimately, the final determined.parking space is determined. Although Although this method this method is isfully fully automatic, automatic, its performance its performance is is better than the previousbetter than the semi-automatic previous semi-automatic method method under under the the condition condition of ofa small a small amount amount of calculation. of calculation.

Figure 4. Hierarchical tree structure of parking space markings. Figure 4. Hierarchical tree structure of parking space markings. Suhr et al. [39] proposed an effective parking space detection and tracking system, whichSuhr is divided et al.into [ 39three] proposedstages. Furthermore, an effective parking space parking marking space detection detection em- and tracking system, ployed the hierarchical tree structure method proposed by Reference [38] to identify var- whichious types is of divided parking space into markings. three stages. The occu Furthermore,pancy classification parking stage of parking space space marking detection employed theuses hierarchicalultrasonic sensor tree data structure to identify th methode vacancy proposed of the detected by parking Reference space. [The38] to identify various types ofparking parking space occupancy space markings. rate calculates Thethe probability occupancy by treating classification each parking space stage of parking space uses area as a single unit occupying the grid. As a result, when the vehicle enters the parking ultrasonicspace, the space data marking to identifythe tracking the stage vacancy continuously of estimates the detected the location parking space. The parking spaceof the selected occupancy parking ratespace. calculatesIn the tracking the process, probability the AVM image by treating and the motion each parking space area as a singlesensor-based unit ranging occupying method are the fused grid. at the As chamfer a result, score level when to achieve thevehicle robustness enters the parking space, to the inevitable occlusion caused by the vehicle. This method can identify the position theand occupancy parking of space various markingparking space the markings tracking in real-time stage and continuously can track stably under estimates the location of the selectedactual conditions. parking The system space. can In help the the tracking driver to quickly process, select thean available AVM parking image and the motion sensor- based ranging method are fused at the chamfer score level to achieve robustness to the

inevitable occlusion caused by the vehicle. This method can identify the position and Symmetry 2021, 13, 128 11 of 18

occupancy of various parking space markings in real-time and can track stably under actual conditions. The system can help the driver to quickly select an available parking space and support the parking control system by continually updating the designated target location.

4.3. Learning-Based Methods Almost all the existing state-of-the-art methods in this field are based on low-level visual features, such as line segments and corner points, and detect some low-level visual algorithms. Due to noise, clutter, or lighting changes caused by non-repeatable environ- mental changes, these features are not significantly distinguishable, and, even worse, they are unstable. Therefore, how to detect parking spaces efficiently and accurately using visual methods in a complex and changeable environment is still a difficult problem. In Symmetry 2021, 13, x FOR PEER REVIEW 12 of 19 order to solve this problem, researchers use deep learning technology to build a neural network model through a learning-based method, train the model from a large number of positive and negativespace and support samples, the parking and control realize system the by detection continually ofupdating parking the designated spaces. tar- Xu et al.get [4 ]location. demonstrated a parking space detection method based on a restricted

coulomb energy4.3. Learning-Based (RCE) neural Methods network color segmentation. According to the inconsistency between the parkingAlmost spaceall the existing marking state-of-the-art line and methods the in background this field are based color, on low-level this method can automaticallyvisual perform features, color such segmentationas line segments and through corner points, the and training detect some of thelow-level REC visual neural network and after adaptivealgorithms. training, Due to noise, so asclutter, to realizeor lighting the changes detection caused by of non-repeatable parking space. environ- Li et al. [40] mental changes, these features are not significantly distinguishable, and, even worse, they illustrated a parkingare unstable. space Therefore, detection how to detect method parking spaces based efficiently on data-driven and accurately using learning. vis- When a surround viewual imagemethods isin a given, complex a and pre-trained changeable environment detector is isstill used a difficult to detectproblem. theIn order marker points, to solve this problem, researchers use deep learning technology to build a neural network and then an effectivemodel through parking a learning-based space is method, inferred train from the model them. from However, a large number due of positive to the influence of changing conditionsand negative such samples, as ground, and realize light, the detection shadow, of parking etc., spaces. it is challenging to detect parking spaces based onXu vision. et al. [4] Intending demonstrated toa parking deal withspace detection this problem, method based Lin on et a al.restricted [41] proposed a coulomb energy (RCE) neural network color segmentation. According to the incon- new parkingsistency space between detection the parking method space basedmarking online aand Deep the background Convolutional color, this method Neural Network (DCNN), namelycan automatically DEEPS, perform which color takes segmentation surrounding through the images training of as the input. REC neural In net- DEEPS, there are two key steps:work and identifying after adaptive alltraining, the so marked as to realize points the detection on the of parking input space. image Li et and al. classifying [40] illustrated a parking space detection method based on data-driven learning. When a the partial imagesurround patterns view image formed is given, bya pre-trained the marked detector points,is used to asdetect shown the marker in Figurepoints, 5. Due to the harsh conditionsand then an of effective outdoor parking parking space is inferred lots andfrom them. the highHowever, reflection due to the influence of indoor parking of changing conditions such as ground, light, shadow, etc., it is challenging to detect park- lots, which reducesing spaces the based reliability on vision. Intending of parking to deal spacewith this detection,problem, Lin et Jang al. [41] et proposed al. [42 a] proposed a unified parkingnew spaceparking detectionspace detection method method based that on can a Deep simultaneously Convolutional Neural detect Network parking spaces composed of(DCNN), multiple namely structures. DEEPS, which This takes method surrounding can images detect as input. a variety In DEEPS, of there objects are through a two key steps: identifying all the marked points on the input image and classifying the fully convolutionalpartial image network patterns of formed semantic by the segmentation. marked points, as Inshown addition, in Figure the 5. Due vertical to the grid coding method can simultaneouslyharsh conditions of outdoor detect parking vacancies lots and identifiedthe high reflection by parkingof indoor parking space lots, markings and which reduces the reliability of parking space detection, Jang et al. [42] proposed a unified vacancies createdparking by space surrounding detection method static that can objects simultaneously without detect sensor parking fusion.spaces composed Huang et al. [43] proposed a parkingof multiple space structures. detection This method method can detect based a variety on of direction objects through marker-point a fully convo- regression, namely Parking-Slotlutional network Detection of semantic Using segmentation. Directional In addition, Marking-Point the vertical grid Regressioncoding method (DMPR-PS). can simultaneously detect vacancies identified by parking space markings and vacancies DMPR-PS doescreated not by use surrounding multiple static off-the-shelf objects without sensor models fusion. but Huang employs et al. [43] a proposed new convolutional a neural networkparking (CNN)-based space detectiondirection method based marker on direction regression marker-point model. regression, Given namely an image, the model can predictParking-Slot the position,Detection Using shape, Directional and Marking-Point direction of Regression each marking (DMPR-PS). point DMPR- on the image. PS does not use multiple off-the-shelf models but employs a new convolutional neural From the markingnetwork points, (CNN)-based geometric direction marker rules regression can be model. used Given to infer an image, the the parking model can space on the image easily. predict the position, shape, and direction of each marking point on the image. From the marking points, geometric rules can be used to infer the parking space on the image easily.

Figure 5. DCNN network structure. Figure 5. DCNN network structure. Hu et al. [44] expounded a deep-learning-based parking space detection method, Hu et al.which [44] sums expounded the loss function a deep-learning-based obtained based on deep learning parking and the cost space function detection of the method, which sums the loss function obtained based on deep learning and the cost function of the template obtained based on template matching to obtain the total cost function. The

Symmetry 2021, 13, 128 12 of 18

position of the point with the highest probability for any model is used as an initial value to search the total cost function to obtain the parking space position. This method combines template matching and deep learning to detect parking spaces in images. It is not only useful when the lighting conditions are good, and the splicing effect is good, but also Symmetry 2021, 13, x FOR PEER REVIEW 13 of 19 when the light source is very complex. In this case, the road is reflective. Moreover, it can realize more robust and accurate parking space detection under poor accuracy conditions.

Furthermore,template obtained based the computational on template matching complexity to obtain the total of cost distance function. transformation The posi- and Canny edge detectiontion of the point is verywith the low, highest and probability the amount for any model of calculation is used as an initial is small. value to Therefore, it can run in real-timesearch the total on cost the function embedded to obtain platform, the parking space thus, position. having This a method direct combines application value. template matching and deep learning to detect parking spaces in images. It is not only useful when the lighting conditions are good, and the splicing effect is good, but also 4.4.when Summary the light source is very complex. In this case, the road is reflective. Moreover, it can realize more robust and accurate parking space detection under poor accuracy conditions. Furthermore,Parking-space-markings-based the computational complexity of distance methods transformation identify and free Canny parking edge spaces by analyzing the markingsdetection is very on low, the and road. the amount Contrary of calculation to free-space-based is small. Therefore, it can methods, run in real- its performance does not dependtime on the on embedded the presence platform, andthus, having location a direct of adjacentapplication value. parked vehicles. However, these methods can4.4. Summary only work normally in parking lots with parking space markings, and it is not robust to situationsParking-space-markings-based where the parking methods space identify markings free parking are spaces blocked by analyzing or blurred. the markings on the road. Contrary to free-space-based methods, its performance does 5.not User-Interface-Based depend on the presence and Methodslocation of adjacent parked vehicles. However, these methods can only work normally in parking lots with parking space markings, and it is not robustFree-space-based to situations where the and parking parking-space-markings-based space markings are blocked or blurred. detection methods require a

tremendous5. User-Interface-Based amount Methods of calculation. Therefore, a parking space method based on a user interfaceFree-space-based is proposed and parking-space-markings-based to solve that problem. detection It supports methods arequire simple a and easy-to-use input method,tremendous such amount as of a calculation. touch screen. Therefore, The a parking user space can specifymethod based a seed on a pointuser for the target location, andinterface the isdetection proposed to solve process that problem. revolves It supports around a simple the seedand easy-to-use point to input search, which significantly method, such as a touch screen. The user can specify a seed point for the target location, reducesand the detection the scope process of revolves detection around andthe seed reduces point to thesearch, amount which significantly of calculation. The steps of this methodreduces the are scope divided of detection into and four reduces steps, the amount as seen of calculation. in Figure The6. steps of this method are divided into four steps, as seen in Figure 6.

Figure 6. User-interface-based methods. Figure 6. User-interface-based methods. Jung et al. [45] designed a seed point-based method, where the driver specifies the targetJung position et through al. [45 a] seed designed point in athe seed rectangular point-based parking space method, marking whereon the the driver specifies the touchscreen-based human-machine interface (HMI) to mark the width of the line segment targetand the seed position point. The through location and a seeddirection point of the parking in the space rectangular are identified parking through space marking on the touchscreen-basedthe intensity gradient of the human-machine direction, and the target interface parking space (HMI) is identified to mark using the the width of the line segment and“T”-shaped the seed connection point. point. The This location method dramatically and direction reduces ofthe thesearch parking range, butspace can are identified through only detect rectangular parking space types [46] based on Reference [27]. The user speci- thefies two intensity seed points gradient on the man-machine of the direction, interface, which and significantly the target reduces parking the scope space is identified using theof the “T”-shaped system search, connection thereby, reducing point. the amount This methodof calculation dramatically and misrecognition. reduces the search range, but Furthermore, through the neural network to classify the connection mode of parking canspace only markings, detect it can rectangular detect many types parking of parking space spaces. types [46] based on Reference [27]. The user specifiesHiramatsu two et seedal. [47] developed points on a rear-view the man-machine camera-based parking interface, assistance which system. significantly reduces the scopeIts functions of the include system “parallel search, parking thereby, assistance” reducing and voice guidance the amount information of calculation super- and misrecognition. imposed on the rear-view camera image. The user can select the “parallel rail” or “normal Furthermore,(such as garage parking)” through mode the through neural the networktouch screen to switch classify on display. the connectionGi et al. [48] mode of parking space markings,use cursor-like it drag-and-drop can detect manyoperations types to move of parkingand rotate spaces.the target position. The methodHiramatsu in Reference [49] et al.uses [ 47an ]interface developed based on a arrow rear-view buttons, camera-basedwhere the driver can parking assistance system. locate the rectangle at the desired parking space by clicking several arrow buttons. As a Itsresult, functions this method include has been “parallelapplied to mass parking production assistance” of a . and voice guidance information super- imposed on the rear-view camera image. The user can select the “parallel rail” or “normal (such as garage parking)” mode through the touch screen switch on display. Gi et al. [48] use cursor-like drag-and-drop operations to move and rotate the target position. The method in Reference [49] uses an interface based on arrow buttons, where the driver can locate the rectangle at the desired parking space by clicking several arrow buttons. As a result, this method has been applied to mass production of a Toyota Prius.

Summary The user-interface-based method finds free parking spaces according to the driver’s manual input of specific information. Although this method requires the driver to have Symmetry 2021, 13, 128 13 of 18

Symmetry 2021, 13, x FOR PEER REVIEW 14 of 19

theSummary disadvantage of repeated operations, it is useful as a backup tool for the failure of the automaticThe user-interface-based method and method it is easyfinds free to parking implement. spaces according to the driver’s manual input of specific information. Although this method requires the driver to have 6.theInfrastructure-Based disadvantage of repeated operations, Methods it is useful as a backup tool for the failure of the automatic method and it is easy to implement. In large-scale parking lot management, the parking space detection method based on infrastructure6. Infrastructure-Based is very Methods suitable, and the target location is specified through the local-global posi- In large-scale parking lot management, the parking space detection method based on tioninginfrastructure system, is very digital suitable, map, and the and target communication location is specified withthrough the the parking local-global management system [50,51]. Thispositioning method system, relies digital on map, the and infrastructure communication with of the the parking parking management lot and sys- needs to distribute various sensortem [50,51]. modules This method and relies user on datathe infrastructure communication of the parking lines lot inand the needs parking to dis- lot in advance, and com- tribute various sensor modules and user data communication lines in the parking lot in municateadvance, and withcommunicate the management with the management system system in thein the parking parking lot lot through through the the vehicle to realize the identificationvehicle to realize the of theidentification parking of space.the parking The space. specific The specific process process is seen is seen in in Figure 7. Figure 7.

Figure 7. Infrastructure-based methods. Figure 7. Infrastructure-based methods. Chen et al. [52] designed an intelligent guidance management system for parking space, Chenincluding et several al. [ 52parking] designed space detectors an intelligentlocated directly guidance above the corresponding management system for parking parking spaces, a parking space controller, several parking space controllers, and a park- space,ing space including guidance controller, several a data parking processing space device, detectors and a data transmission located directly network. above the corresponding parkingThe parking spaces, guidance a controller parking is connected space controller, with a number several of parking parking guidance space screens controllers, and a parking spaceand a parking guidance indicator controller, box set at each a dataparking processing space. The parking device, space and detector a data detects transmission network. The whether there is parking in the corresponding parking space. First, it transmits corre- parkingsponding signals guidance to the parking controller space iscontro connectedller, and then with the aparking number space of controller parking guidance screens and a parkingobtains the indicatorbest parking boxspace set and at displays each parkingit on the parking space. space The guide parking screen. Corre- space detector detects whether therespondingly, is parking the parking in space the indicator corresponding box that controls parking the corresponding space. First, parking it transmits corresponding space flashes. After that, the user can be guided to quickly and accurately park the car into signalsthe allocated to parking the parking space. Yan space et al. [53] controller, proposed a safe and and then intelligent the parking system space controller obtains the bestbased parkingon a secure spacewireless andnetwork displays and sensor it communication. on the parking The parking space process guide is screen. Correspondingly, modeled as a random process. The vehicle broadcasts in the field and then uses the sensor theinfrastructure parking and space decryption indicator methods to box protect that the privacy controls of thethe driver corresponding and the security parking space flashes. Afterof information. that, the user can be guided to quickly and accurately park the car into the allocated parkingLi et al. space. [54] designed Yan etan al.intelligent [53] proposed reservation management a safe and system intelligent for parking parking system based on a spaces. This system includes an intelligent parking guidance unit, parking space reserva- securetion management wireless unit, network and control and background sensor. In communication. addition, the intelligent The parking parking guid- process is modeled as a randomance unit mentioned process. above The includes vehicle multiple broadcasts sets of level-guided in the field screens. and The then first park- uses the sensor infrastructure anding space decryption detector is installed methods in the to roadside protect parking the privacy space, andof the the geomagnetic driver andvehicle the security of information. Li et al. [54] designed an intelligent reservation management system for parking spaces. This system includes an intelligent parking guidance unit, parking space reser- vation management unit, and control background. In addition, the intelligent parking guidance unit mentioned above includes multiple sets of level-guided screens. The first parking space detector is installed in the roadside parking space, and the geomagnetic vehicle detector is installed at the entrance and exit of the parking lot. The parking space reservation management unit includes the parking space reservation terminal and the parking space entrance, exit, and license plate recognition device on top of each parking space. Additionally, the second parking space detector and liftable ground lock for each parking space in the parking lot, data collection terminal, and guide screen for reserved parking space. The utility model can display the remaining parking space status of the parking lot in real-time. It can accurately indicate the parking space location, reserve the parking space in advance, prevent the reserved parking space from being occupied by others, and guide the car owner to go to the reserved parking space quickly. Liu et al. [55] developed a global positioning system (GPS)-based automatic parking assistance method and system for unmanned vehicles, which is suitable for side parking, Symmetry 2021, 13, 128 14 of 18

involving: collecting GPS location information of parking spaces in the target area, obtain- ing vehicle information of minimum turning radius information, and braking distance information. According to the GPS location information, it can mark the parking space, and the parking space is marked according to the parking space information to mark the entrance midpoint of the parking space. According to the parking space entrance midpoint, minimum turning radius information and braking distance information set the turning point and deceleration point. According to the preset turning angle and the turning point and deceleration point, the optimal driving trajectory before parking is generated. By using GPS to set auxiliary points, we guide the car’s position, angle and speed to the appropriate value when entering the parking space, generate the optimal trajectory before parking, reduce uncertainty, and improve parking accuracy. Xie [56] designed a GPS-based smart parking navigation system, including a GPS navigation device for the car, a parking lot management server, and a parking detector configured in each parking space. Information is transmitted between the parking lot management server and parking spaces through the mobile communication system. The parking space detector transmits the detected parking space information to the parking lot management server. When looking for a parking space, the car driver needs to send the parking through the mobile communication system first. The requested information is sent to the parking lot management server. The parking request information contains the longitude and latitude information of the current location of the car, and the parking lot management server compares the parking request information with the parking space information and calculates the nearest parking space. After that, the result is transmitted to the car GPS navigation device through the mobile communication system. Finally, the car GPS navigation device navigates the car to the nearest parking space. This method can solve the problem of finding a parking space. Cao et al. [57] provided a parking space guidance system for drones based on the , which includes a PC-side upper computer and a drone. Among them, the PC-side upper computer includes a parking guide model part, a real-time display part of Unmanned Air Vehicle (UAV) coordinates and wireless serial port remote control part. The design has good practicability and can be modified on existing equipment. The drone recognizes empty parking spaces through images. The upper computer automatically plans the optimal path and assigns the drone to guide the incoming vehicles to the corresponding parking spaces and return. The invention is suitable for large-scale and busy open-air parking because it can guide parking in a targeted manner, avoiding the trouble of finding parking space for vehicles and solving parking space grabbing. It links the warehousing vehicles together through drones and can rationally allocate parking resources to achieve energy saving and emission reduction.

Summary Different from other methods, infrastructure-based methods use pre-built maps and underlying sensors to specify the target location through vehicle-to-infrastructure com- munication, using equipment outside the vehicle to help the driver easily find or reserve a parking space. Although these methods have the advantage of managing all parking spaces, they are limited to parking lots with pre-established infrastructure.

7. Summary and Outlook In recent years, with the continuous improvement of drivers’ requirements for cars, the parking assistance system has been developed rapidly, and the parking space detection technology has also been improved. According to the different critical technologies of parking space detection, this paper elaborates on the related research of parking space detection. It analyzes and introduces parking space detection methods based on free space, parking space marking, user interface, and infrastructure. At present, the mainstream methods are based on free space and parking space markings because these two methods achieve the purpose of low-cost and fully automatic parking, but, at the same time, there Symmetry 2021, 13, x FOR PEER REVIEW 16 of 19

a parking space. Although these methods have the advantage of managing all parking spaces, they are limited to parking lots with pre-established infrastructure.

7. Summary and Outlook Symmetry 2021, 13, 128 In recent years, with the continuous improvement of drivers’ requirements for cars, 15 of 18 the parking assistance system has been developed rapidly, and the parking space detec- tion technology has also been improved. According to the different critical technologies of parking space detection, this paper elaborates on the related research of parking space detection. It analyzes and introduces parking space detection methods based on free are certain shortcomings.space, parking space Among marking, them, user interface, the detection and infrastructure. efficiency At present, of parking the main- spaces depends entirely on thestream neighboring methods are based posture. on free Whenspace and there parking are space no markings neighboring because these vehicles, two it is easy to cause a systemmethods detection achieve the failure. purpose As of low-cost we all and know, fully theautomatic research parking, object but, at ofthe the same parking-space- time, there are certain shortcomings. Among them, the detection efficiency of parking marking-basedspaces method depends entirely includes on the parking neighboring space posture. markings. When there are Therefore, no neighboring when ve- the parking space markingshicles, it are is easy worn to cause or a occluded system detection seriously, failure. As thewe all system know, the will research also object fail of to detect. In the parking-space-marking-based method includes parking space markings. Therefore, addition, thiswhen detection the parking is space not robustmarkings when are worn it or is occluded in a dark seriously, environment. the system will Figure also fail8 summarizes some methodsto detect. and In key addition, technologies this detection involved is not robustin when this it is article. in a dark environment. Figure 8 summarizes some methods and key technologies involved in this article.

Figure 8. Summary of parking space detection methods. Figure 8. Summary of parking space detection methods. With the continuous development of sensor technology and artificial intelligence With thedeep continuous learning technology, development sensor technology of is sensor also developing technology in the direction and of artificial artificial intelligence deep learningintelligence. technology, Intelligent sensor sensors technology can integrate data is also collection, developing storage, and in processing, the direction and of artificial have functions such as independent selection and self-regulation, which will provide an intelligence.excellent Intelligent technical sensors solution canfor parking integrate space detection data collection, methods. This storage, article believes and that processing, and have functionsfurther such research as independentcan be promoted from selection the following and aspects self-regulation, in the future. which will provide an excellent technical1. Research solution on Multi-Sensor-Fusion-Based for parking space Parking detection Space Detection methods. This article believes that The algorithms mentioned in this article are all based on the premise of a single sen- further researchsor. Moreover, can be when promoted it comes fromto the practicality the following of the algorithm, aspects the in detection the future. system 1. Researchwith on only Multi-Sensor-Fusion-Based a single sensor has inherent limitations, Parking such as Space the difficulty Detection of tracking the camera when the camera moves quickly, and it is difficult to deal with dynamic obstacles. The algorithms mentioned in this article are all based on the premise of a single

sensor. Moreover, when it comes to the practicality of the algorithm, the detection system with only a single sensor has inherent limitations, such as the difficulty of tracking the camera when the camera moves quickly, and it is difficult to deal with dynamic obstacles. Therefore, fusing different sensor data to complement each other can make the system more robust and accurate. For example, the detection system of the inertial navigation combination is more suitable for complex scenes. The inertial sensor (IMU) can measure the acceleration and angular velocity of the sensor body and complements the camera sensor. After the fusion of the two, a more complete parking space detection system can be obtained. However, how to effectively combine the two is a problem worthy of in-depth discussion. 2. Research on Artificial-Intelligence-Based Parking Space Detection With the continuous development of artificial intelligence technology, the introduction of artificial intelligence technology into the parking space detection method can make the parking space detection process more intelligent. Xu et al. proposed a color-segmentation- based parking space detection method that relies on a REC neural network, which is the early research that used artificial intelligence technology to detect parking spaces. This method provides excellent guidance for parking space detection and obstacle detection. Whether artificial intelligence technology can be applied to more parking space detection methods is an important research topic. Symmetry 2021, 13, 128 16 of 18

3. Research on Parking Space Detection Integrated with Task Requirements The detection of parking spaces is not the ultimate goal. However, it is to complete diversified tasks by accurately detecting parking spaces, which puts forward higher re- quirements for detecting parking spaces. When detecting parking spaces, it is necessary to use the completion of the task as an indicator of detection. K. Hamada et al. [30] made a similar attempt. They regard the ground as a plane, detect parking spaces in the collected images, and estimate the two-dimensional rotation of the target in the current frame and the previous frame. It is panning to realize the tracking processing of the parking space in the panoramic image to complete the selection of the best parking space for the parking operation in the case of multiple parking spaces.

Author Contributions: Y.M. is the author of the thesis and directs the writing of the thesis; Y.L. is the main writer of the thesis; L.Z., Y.C. and S.G. participated in the analysis and sorting of the literature; H.L. participated in the proofreading and finalization of the thesis. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by [Jiangxi Province National Science and technology award reserve project cultivation plan] grant number [20192AEI91005], And The APC was funded by [Jiangxi Province National Science and technology award reserve project cultivation plan]. Institutional Review Board Statement: Not applicable Informed Consent Statement: Not applicable Data Availability Statement: Data sharing not applicable Conflicts of Interest: The authors declare no conflict of interest

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