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Review on Vehicle Detection Technology for Unmanned Ground Vehicles sensors Review Review on Vehicle Detection Technology for Unmanned Ground Vehicles Qi Liu 1, Zirui Li 1,2 , Shihua Yuan 1,*, Yuzheng Zhu 1 and Xueyuan Li 1 1 School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100089, China; [email protected] (Q.L.); [email protected] (Z.L.); [email protected] (Y.Z.); [email protected] (X.L.) 2 Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands * Correspondence: [email protected]; Tel.: +86-10-6891-1307 Abstract: Unmanned ground vehicles (UGVs) have great potential in the application of both civilian and military fields, and have become the focus of research in many countries. Environmental perception technology is the foundation of UGVs, which is of great significance to achieve a safer and more efficient performance. This article firstly introduces commonly used sensors for vehicle detection, lists their application scenarios and compares the strengths and weakness of different sensors. Secondly, related works about one of the most important aspects of environmental perception technology—vehicle detection—are reviewed and compared in detail in terms of different sensors. Thirdly, several simulation platforms related to UGVs are presented for facilitating simulation testing of vehicle detection algorithms. In addition, some datasets about UGVs are summarized to achieve the verification of vehicle detection algorithms in practical application. Finally, promising research topics in the future study of vehicle detection technology for UGVs are discussed in detail. Keywords: unmanned ground vehicles; sensor; vehicle detection; simulation platform; dataset Citation: Liu, Q.; Li, Z.; Yuan, S.; Zhu, Y.; Li, X. Review on Vehicle Detection Technology for Unmanned Ground Vehicles. Sensors 2021, 21, 1354. https://doi.org/10.3390/ 1. Introduction s21041354 The unmanned ground vehicle (UGV) is a comprehensive intelligent system that integrates environmental perception, location, navigation, path planning, decision-making Academic Editor: Felipe Jiménez and motion control [1]. It combines high technologies including computer science, data fusion, machine vision, deep learning, etc., to satisfy actual needs to achieve predetermined Received: 15 January 2021 goals [2]. Accepted: 10 February 2021 In the field of civil application, UGVs are mainly embodied in autonomous driving. Published: 14 February 2021 High intelligent driver models can completely or partially replace the driver’s active con- trol [3–5]. Moreover, UGVs with sensors can easily act as “probe vehicles” and perform Publisher’s Note: MDPI stays neutral traffic sensing to achieve better information sharing with other agents in intelligent trans- with regard to jurisdictional claims in port systems [6]. Thus, it has great potential in reducing traffic accidents and alleviating published maps and institutional affil- traffic congestion. In the field of military application, it is competent in tasks such as acquir- iations. ing intelligence, monitoring and reconnaissance, transportation and logistics, demining and placement of improvised explosive devices, providing fire support, communication transfer, and medical transfer on the battlefield [7], which can effectively assist troops in combat operations. Copyright: © 2021 by the authors. The overall technical framework for UGVs is shown in Figure1. It is obvious that Licensee MDPI, Basel, Switzerland. environmental perception is an extremely important technology for UGVs, including the This article is an open access article perception of the external environment and the state estimation of the vehicle itself. An en- distributed under the terms and vironmental perception system with high-precision is the basis for UGVs to drive safely and conditions of the Creative Commons perform their duties efficiently. Environmental perception for UGVs requires various sen- Attribution (CC BY) license (https:// sors such as Lidar, monocular camera and millimeter-wave radar to collect environmental creativecommons.org/licenses/by/ 4.0/). information as input for planning, decision making and motion controlling system. Sensors 2021, 21, 1354. https://doi.org/10.3390/s21041354 https://www.mdpi.com/journal/sensors Sensors 2021, 21, x FOR PEER REVIEW 2 of 49 sensors such as Lidar, monocular camera and millimeter-wave radar to collect environmen- Sensors 2021, 21, x FOR PEER REVIEWtal information as input for planning, decision making and motion controlling 2system. of 49 Sensors 2021, 21, 1354 2 of 46 sensors such as Lidar, monocular camera and millimeter-wave radar to collect environmen- tal information as input for planning, decision making and motion controlling system. Figure 1. Technical framework for UGVs. Environment perception technology includes simultaneous localization and map- pingFigure (SLAM 1. Technical), semantic framework segmentation, for UGVs.UGVs. vehicle detection, pedestrian detection, road detec- tion and many other aspects. Among various technologies, as vehicles are the most nu- Environment perceptionperception technology technology includes includes simultaneous simultaneous localization localization and and mapping map- merous and diverse targets in the driving environment, how to correctly identify vehicles (SLAM),ping (SLAM semantic), semantic segmentation, segmentation, vehicle vehic detection,le detection, pedestrian pedestrian detection, detection, road road detection detec- has become a research hotspot for UGVs [8]. In the civil field, the correct detection of road tionand manyand many other other aspects. aspects. Among Among various various technologies, technologies, as vehicles as vehicles are the are most the numerous most nu- vehiclesmerousand diverse andcan diversereduce targets targets intraffic the drivinginaccidents the driving environment,, build environment, a more how complete tohow correctly to correctly ADAS identify [9,10identify vehicles] and vehicles achieve has bet- terhasbecome integration become a research a research with hotspot driver hotspot formodel for UGVs UG [11,12Vs [8]. [8] In]., Inwhile the the civil civil in field,the field, field the the correct ofcorrect military, detection detection the ofcorrectof roadroad detec- tionvehicles of military cancan reducereduce vehicle traffictraffic targets accidents, accidents is , build ofbuild great a a more more significance complete complete ADAS ADASto the [9 [9,10,battlefield10] and] and achieve achieve reconnaissance, better bet- threatterintegration integration assessment with with driver and driver accurate model model [11 attack ,[11,1212], while] in, while modern in thein the field warfare field of military,of [13]military,. the the correct correct detection detec- tionof military Theof military complete vehicle vehicle targetsframework targets is of great isof ofvehicle significancegreat recognitionsignificance to the tobattlefieldin theUGVs battlefield autonomous reconnaissance, reconnaissance, driving threat system assessment and accurate attack in modern warfare [13]. isthreat portrayed assessment in Figure and accurate 2. Generally, attack vein hiclemodern detection warfare is[13] used. to extract vehicle targets in a The complete framework of vehicle recognition in UGVs autonomous driving system singleis portrayed frame inof Figurean image,2. Generally, vehicle vehicle tracking detection aims isto usedreidentify to extract positions vehicle targetsof the invehicles a in is portrayed in Figure 2. Generally, vehicle detection is used to extract vehicle targets in a subsequentsingle frame frames, of an image, vehicle vehicle behavior tracking prediction aims to refers reidentify to characterizing positions of the vehicles’ vehicles inbehavior single frame of an image, vehicle tracking aims to reidentify positions of the vehicles in basingsubsequent on detectio frames,n vehicle and tracking behavior in prediction order to refers make to a characterizing better decision vehicles’ for ego behavior vehicle [14]. subsequent frames, vehicle behavior prediction refers to characterizing vehicles’ behavior Forbasing tracking on detection technology, and tracking readers in can order refer to make to [15,16 a better], while decision for forvehicle ego vehicle behavior [14]. prediction, For basing on detection and tracking in order to make a better decision for ego vehicle [14]. tracking technology, readers can refer to [15,16], while for vehicle behavior prediction, [17] [17]For presentedtracking technology, a brief review readers on can deep refer-learning to [15,16-]based, while methods. for vehicle This behavior review prediction, paper focuses presented a brief review on deep-learning-based methods. This review paper focuses on[17] the presented vehicle detectiona brief review component on deep- learningamong the-based complete methods. vehicle This review recognition paper focusesprocess, sum- on the vehicle detection component among the complete vehicle recognition process, marizeson the vehicle and discusses detection componentrelated research among theon completevehicle detection vehicle recognition technology process, with sum- sensors as summarizes and discusses related research on vehicle detection technology with sensors as themarizes main andline .discusses related research on vehicle detection technology with sensors as the main line. the main line. Figure 2. The overall framework of vehicle recognition technology for unmanned ground vehicles (UGVs). Sensors
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