The Study on Innovation, Development and Implementation

ISSN (Online) 2581-9429 IJAR SCT ISSN (Print) 2581-XXXX

International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 11, Issue 1, November 2020 Impact Factor: 4.819

The Study on Innovation, Development and Implementation of the Self-driving Car Aniruddha Chaki Student, Department of Electrical Engineering Siliguri Institute of Technology, Siliguri, India

Abstract: The development of the self-driving car is one of the greatest inventions of the century. With the technological advancement, the implementation of the autonomous vehicle has become the major attention among the researchers. This paper discusses the brief history of self-driving cars, its research and development, key technologies behind the self-driving, main aspects of implementing self-driving cars. The paper also describes some challenges with possible solutions to the fully autonomous vehicle in mass implementation.

Keywords: Advanced Driver Assistance System (ADAS), autonomous, navigation, positioning, self- driving technology, radar, lidar, path planning, obstacle avoidance, Full Self driving (FSD), accidents, protests, advantages, challenges.

I. INTRODUCTION In this 21st-century progress in technology has made man as omnipotent, omniscient and omnipresent as a god. It is being tried to find out and accomplish any work with higher efficiency, lesser cost, and least possible effort. This makes researchers explore the domain of Automation, Machine learning, and Artificial Intelligence to handle the hardest of the jobs which were once tedious and cumbersome for humans [1]. We are driving towards the future where humans will only be involved in high mental ability skills and all the other works will be automated [2]. In this paper, the previous researches in the field of self-driving have been studied. In early years i.e. 1920-1950 the progress was limited but showed a great scope of further research. The primitive autonomous vehicles were tested on a special road and atmosphere, which was not quite practical. Further researches and experiments improved the autonomous vehicles drastically and also gained funding [3]. From the 1980s the development has grown rapidly and researches conducted contributing to the advanced Self-driving technology. Presently Autonomous vehicles up to level 3 are on the market, which can drive itself in many conditions [4]. The Technology behind self-driving is studied in the paper. The key area of technology includes navigation, planning, and control. The vehicle has to navigate the path leading to the destination, then the vehicle plans the suitable path leading to the destination and the driving is controlled or assisted autonomously. Whenever a new technology or a new concept is introduced, it faces plentiful challenges for mass implementation; this is not exceptional for self-driving cars as well. The different prospects of self-driving cars, the kind of future it drives us into, what is its future, this type of fact arises when we talk about self-driving cars. The technical challenges like the unequivocal detection of obstacles at high speeds and long distances are one of the greatest difficulties to face [5]. The controller of the system must be very robust and advanced to ensure the ability of the car to run in any condition of the road in different weather. This paper focuses on the technology behind self-driving cars, implementation of this on a large scale and what could be its future. Challenges like decreased career options in driving and controlling, Artificial Intelligence related issues [23][25] are studied in this paper.

II. SURVEY OF PREVIOUS RESEARCHES ON SELF-DRIVING CARS The progress of autonomous cars started in the 20th century back in the 1920s. Trials and researches have been conducted since then. A company named Houdina Radio Control demonstrated a remote-controlled car known as American Wonder (a 1926 Chandler equipped with transmitting antenna and tonneau) in New York, which Copyright to IJARCST DOI: 579.112020/2581 59 www.ijarsct.co.in ISSN (Online) 2581-9429 IJAR SCT ISSN (Print) 2581-XXXX

International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 11, Issue 1, November 2020 Impact Factor: 4.819

travelled through the city in a thick traffic jam [3]. In 1939 World’s Fair, a radio-controlled electric car controlled by the electromagnetic field provided by circuits embedded in the road was depicted by Norman Bel Geddes who promoted advances in highway design and transportation and predicted that humans will be one day removed from the process of driving [6]. In 1957, RCA Labs and the State of Nebraska demonstrated a system of a driverless car on a 400 foot stretch on a public highway of Nebraska Highway in US. A series of experimental detector circuits were embedded in the street and a series of lighting along the edge of the road [7]. Dr. Robert L. Cosgriff in 1960 launched a project to develop autonomous cars in the Communication and Control Systems library of Ohio State University [8]. It was claimed in 1966 that this system could be ready for mass application in the next 15 years. In August 1961, William Bertelsen invented an air-cushion vehicle (ACV) named Aeromobile 35B which was reported on Popular Science. It was created to revolutionize the transportation system, with personal autonomous cars that could reach up to the speed of 1,500MPH [9][10]. The first self-sufficient and truly autonomous car prototype makes its debut in the Navlab of Carnegie Mellon University in the 1980s. Also in 1987 German automaker giant Mercedes Benz collaborated with Bundeswehr University Munich, and make Eureka PROMETHEUS Project [11]. It was one of the largest R&D projects in the field of autonomous vehicles, receiving funding of whooping 749 million Euros. Numerous universities and car companies participated in this Pan-European project. In 1989, Carnegie Mellon University (CMU) demonstrated the use of neural networks to steer and control autonomous vehicles, which formed the basis of contemporary control strategies [10]. The ISTEA Transportation Authorization bill was passed by the United States Congress in the same year. Following this bill United States Department of Transportation, organized a depiction of autonomous vehicles. This cost-shared project was led by General Motors and FHWA; with Delco, Caltrans, Bechtel, University of California-Berkeley, Parsons Brinkerhoff, CMU and Lockheed Martin as additional partners. Daimler- Benz and Ernst Dickmanns of UniBwM tested twin-bot vehicles VaMP and Vita-2 in 1995 on more than 1000km on a 3-lane highway in standard heavy traffic with human intervention i.e. semi-autonomously [12]. In the same year, Dickmanns’ re-engineered Mercedes Benz S-Class was autonomously operated 1590km long journey from Munich, Germany to Copenhagen, Denmark and back, using Saccading computer vision and transputers in real-time [12]. In 1996, Italy, Prof Alberto Broggi of University of Parma drove a modified Lancia Therma, following painted lane marks in unmodified highway in a stretch of 1900km on the highways of northern Italy dubbed Miglia in Automatico, with an average speed of 90km/h with 94% of its path being automated [13]. In March 2004, the Defense Advanced Research Project Agency (DARPA), held the first Grand Challenge offering a $1 million prize for engineers who could create an automatic vehicle capable of finishing 150-mile stretch on the Mojave Desert [14]. None were successful in the first year, but next year five cars completed the course. Willie Jones states that many automakers are enthusiastic about self-driving technology and semi-autonomous driving [15]. In May 1998, Toyota first introduces Automatic Cruise Control in their vehicle. Google also started developing its self- driving car project in 2009 [16]. Many major automobile manufacturers, including Mercedes Benz, General Motors, Volkswagen, Ford, Audi, Toyota, Nissan, BMW, Volvo all are testing driverless car systems. Bayerische Motoren Werke (BMW) has been testing driverless systems since 2005. In 2010, the University of Parma, Italy, led by Prof. Alberto Broggi, successfully drove four electric vans leaving Parma, Italy to Shanghai Expo in China, completing a 15900km test run [13]. In 2010, Germany licensed its first autonomous car on the streets. It was built by Technische Universitat Braunschweig and was a modified research vehicle Leonie [17]. Oxford University introduced its WildCat Project in 2011 [18], creating an automatic car from modified Bowler WildCat. In 2012, US state Nevada passed a law for testing autonomous vehicles. A modified Toyota Prius was the first licensed Automated vehicle in Nevada, US. Also in the same year, Florida and California allowed the testing of driver-less cars [19]. Stanford’s Dynamic Design Lab produced Shelly, a modified Audi TTS, for the track used for a speed greater than 160km/h [20]. In April 2015, Delphi Automotive designed a self-driving car that completed the first coast to coast journey from San Francisco to New York, under computer control in 99% of the distance [21]. Tech-giant Apple started its autonomous vehicle (iCar) project called Project Titan in 2015 [22]. In April 2019, Tesla unveiled its Full Self-Driving (FSD)

International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 11, Issue 1, November 2020 Impact Factor: 4.819

computer which will be installed in their cars to enable fully autonomous driving with minimal effort from the human driver [23].

III. TECHNOLOGY BEHIND THE AUTONOMOUS VEHICLE Compared to manual driving, automatic vehicles have many separate aspects of technology. Based on the characteristic and functional requirement on driving and on-board equipment module, according to SAE, the core technology of self-driving cars can be classified into 6 categories [24]: Level 0 There is no automatic technology in this level. The driving solely depends on the human driver. Level 1 This is the first level of automation. The Advanced Driver Assistance System (ADAS) assists the human driver with steering, braking, accelerating, though not simultaneously. The driver has to drive the car himself with limited assistance from the ADAS technology. Level 2 The ADAS is more advanced in this level. It can steer and either brake or accelerate simultaneously while the driver remains fully aware behind the wheel and continues to act as the driver. Level 3 An Automated Driving System (ADS) can perform all driving tasks under certain circumstances, such as parking the car, driving in the highways with lane following etc. In these circumstances, the human driver must be ready to re-take control whenever the system faces any difficulties and is still required to be the main driver of the vehicle. Level 4 An ADS is able to perform all driving tasks and monitor the driving environment in certain circumstances. In those circumstances, the ADS is reliable enough that the human driver needn't pay attention. Level 5 In this level the vehicle's ADS acts as a virtual chauffeur and does all the driving in all circumstances. Steering wheels, brakes and other controllers are optional. Occupants need not to face the windscreen. The human occupants are passengers and are never expected to drive the vehicle. Table 1: The Classification of the Self-Driving Cars 1. Navigation System: The car needs to navigate through the streets and make its way to the destination. Geographic Information System and Global Positioning System (GPS) work to achieve the exact location of the vehicle, by latitude and longitude, digital map database, etc. Intelligent path mapping algorithms (e.g. Dijkstra’s Algorithm, Bellman-Ford Algorithm) play a key role in detecting a suitable route. After the calculation of the location of the car and destination, the car can be programmed by specific algorithms, to find its way by path planning model. 2. Positioning System: It determines the location of the vehicle, which can be of absolute location or relative location. Inertial Navigation System (INS) is a common relative positioning system (by Farrell and Barth, 1999) which determines the automotive’s angular velocity and absolute velocity by gyroscope and accelerometer. Using this data different types of calculations like relative course angle, speed can be computed. The absolute location can be determined from satellite-based systems like GPS, GLONASS, Galileo, Biedou, etc. The Hybrid location which is the combination of absolute and relative location is mostly used in self-driving cars. Gmouse UB-353 GPS model, analog device ADIS16300 INS, SPAN- CPT integrated navigation system, SPAN-FSAS fractional navigation system, etc are used for complex calculation and determination of the Hybrid positioning system. 3. Mapping: This mainly comprises the geographical characteristics of the area, traffic information, buildings, traffic signs, signals, road facilities, parking areas, etc. The electronic mapping system helps the vehicle to find its way to the destination more efficiently. Nowadays major car-makers and tech-giants are working on self- driving car specific technologies like automatic road sign detection system, interactions between self- driving cars and pedestrians, etc. Electronic maps for automated vehicles have three layers, viz, (i) Active layer, which is the main map containing road-level data, lane attributes, elevated objects, etc. (ii) Dynamic layer, which updates with the real-time traffic data. It is the network integration-collaborative perception. Copyright to IJARCST DOI: 579.112020/2581 61 www.ijarsct.co.in ISSN (Online) 2581-9429 IJAR SCT ISSN (Print) 2581-XXXX

International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 11, Issue 1, November 2020 Impact Factor: 4.819

(iii) Analysis layer, which trains the self-driving car by analyzing the real-time big data of human driving records. The current HD map is of ADAS level, which helps the technology in the level 2-3 self-driving vehicles.

Figure 1: Block Diagram Representation of Self-Driving Mechanism 4. Map Matching: This is the process of calculating the vehicle’s location by the data available from the Map. Advanced GPS systems play a key role in this field. 5. Path Planning: It is used to determine the most efficient and optimal driving way to get to the destination. This technology is currently in use in major scale in the various driving applications and maps. 6. Laser Perception: Laser is used as a radar system. In laser perception, continuous or discrete laser beams are used to determine any obstacle; object information, distance, size, velocity can be calculated out. The LIDAR technique, i.e. mapping and scanning in 3D technology, is used in self-driving cars. Velodyne LiDAR Inc. A major company of the lidar system produces HDL-64E, HDL-32E, VLP-16. These are used in automated vehicles too. 7. Radar Perception: Radar system is used for distance detection, which is achieved by computing the return time of millimeter waveform. DENSO, Bosch, TRW, Delphi, Hella these companies develop industrial level radar systems. The latest product LRR4 can detect moving vehicles 250 meters away. 8. Visual Perception: Nowadays all the traffic signals and systems are designed for human drivers. It becomes important for self-driving cars to interpret all these signals. There are mainly two navigation systems; one is Simultaneous Localization And Mapping (SLAM) and another is visual understanding based on the captured image. However, to make the automatic vehicles market-ready, the radar and visual sensors must have to be robust and reliable. 9. Driving Control: The vehicle direction control method consists of longitudinal and lateral control. Longitudinal control can be achieved by advanced control methods formulated by driving experiences. The lateral control algorithm based on a composite of a Cerebellar Model Articulation Controller and PID controller. Combined with the longitudinal control, the system can compensate automatically even when the model and input signal change quickly and unpredictably, and thus the autonomous vehicle can drive itself accurately and in a foolproof manner in any driving condition and environment [25].

IV. PRACTICAL IMPLEMENTATION OF AUTONOMOUS VEHICLES No research and development are successful until we can apply them in the real world. The revolutionary project of self-driving cars or autonomous vehicles should take over the roads of the world one day. Many companies are also testing and trying to implement automated cargo vehicles. Uber acquired a self-driving trucking company, Otto, in August 2016. Otto demonstrated self-driving trucks on highways [26]. San Francisco based company Starsky Robotics completed a 7 mile fully driverless drive in Florida in a self-driving truck in March 2018 [27]. In summer 2015, the French government has allowed PSA Peugeot-Citroen to test and trial their self-driving cars in Paris in real conditions. Cities in Belgium, France, Italy, UK, etc are planning to operate transport systems by self-driving cars [28]. New Zealand is also in the line as it is planning to use autonomous vehicles in public transport system in cities like

International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 11, Issue 1, November 2020 Impact Factor: 4.819

Tauranga and Christchurch. China is on the way of implementing level 4 autonomous minibuses without driving seats, which will be made by Baidu and King Long [29]. Russian internet company Yandex is developing driverless cars. They tested a prototype of their autonomous vehicle Yandex-taxi in 2018, Moscow [30]. In October 2015, Tesla Automobiles launched its autopilot system in US. This system allowed the vehicle to steer itself under certain conditions in highways for a limited period. It also allowed the vehicle to self-park itself [23]. Tech giant Google has already tested 2400000 km of their self-driven car under the banner of the company Waymo. Google and Waymo will remain as the subsidiaries of the parent company Alphabet [31]. By December 2017, Uber has completed its successful test of 2 million miles in automated mode. Electric Car Company Tesla stated that they are shipping the cars with all the required hardware for self-driving already installed. Only the future OTA software updates will enable full autonomy. In April 2019, Tesla announced the Full Self-Driving (FSD) computer which is already in production and will be available soon. This technology will enable complete autonomy in cars. Tesla also introduced Smart Summon in October 2019, which allows the car to drive itself from parking to the owner with no driver inside just via the mobile application [32].

A) Success and Advantages of Self-Driving Cars Self-driving cars will gift humans a more convenient and relaxed travelling experience. Without any drivers, cars would transform into a mini leisure room with more spaces and high-end entertainment technologies like video screens, gaming consoles, workspaces, etc. Travellers can travel overnight and sleep for the duration without any hassle of driving. Without being involved in driving a vehicle, people can indulge in more productive activities like board meetings, studying, working, etc. This will be time-saving and increase the productivity of mankind as a whole [33]. It is predicted by the driving safety analysts that once successful application of the self-driving technology everywhere, the rate of accidents and incidents due to human errors will be drastically reduced. Consulting firm McKinsey & Co estimated that the widespread use of autonomous vehicles will reduce the accident rates in the US 90% and can also save $190 billion in damages and health-costs and thousands of lives annually [34]. The labour cost will also reduce largely because of less human force needed for self-driving operation. Self-driving cars will be also helpful for physically or mentally challenged people to drive or ride cars, making the journey easier. For the young, aged people with disabilities and low and middle-class citizens, autonomous cars could provide enhanced mobility. Higher speed limits, smoother and efficient rides, increased roadway capacity, low insurance cost, minimized traffic congestion, increased safety, etc are the other benefits of the autonomous vehicles [35]-[37]. One study about road capacity and cars estimates that automated cars can increase the capacity of the road by 273%. The study also reveals that with the 100% connected cars on the road using the vehicle to vehicle communication; the capacity could reach up to 12000 passengers per vehicle per hour which is up to 545% higher than the current rate 2200 pc/h per lane. This increases the highway capacity significantly to reduce the enormous traffic congestion especially in the urban areas [38]. The ability of the authorities to manage the traffic will also drastically improve, given the extra data and driving behaviour predictability combined with the less use of traffic controllers. The self-driving car can also help improve the fuel economy by cutting down the unnecessary use of the engine hereby improving the drive cycle [39]. An automated vehicle can brake and accelerate more efficiently than a normal car, this also helps improve better fuel economy, improved mileage, by reducing the wasted energy typically associated with inefficient changes in speed. The modern self-driving technology will most likely be implemented on fully electric cars, which have zero emissions and increased efficiency. The vehicles’ increased intelligence could aid law enforcers by reporting on illegal passenger behaviour; reduce crimes like intentionally crashing into other people or cars [40].

V. CHALLENGES FOR THE MASS IMPLEMENTATION OF SELF-DRIVING CAR AND POSSIBLE SOLUTIONS  The direct impact of the widespread adaptation of self-driving technology is the loss of driving-related jobs in the transport industry [41]. The automobile industry may suffer as the many related jobs like drivers, public transit services, crash repair shops etc, may become obsolete. A frequently cited paper by Michael Osborne and Carl Benedikt Frey found that automated cars would make many jobs redundant [42]. This problem can be

International Journal of Advanced Research in Science, Communication and Technology (IJARSCT)

Volume 11, Issue 1, November 2020 Impact Factor: 4.819

handled by creating more job opportunities in other fields like Machine Learning, A.I and Computing. There will be also increased demand for new skilled mechanics and labours on the automation Industry.  There is also an emerging privacy-related concern as all connected cars can be tracked and other people can have access to the vehicle’s position in real-time. [43] Major automobile manufacturers and Technology giants must value customers’ privacy and protect personal data under complete control of consumers.  The risk of terrorist attacks is also an issue with the possibility of automated cars can be loaded with explosives and used as bombs. [44]  There is also a huge chance of the hacking of the car’s self-driving software system. Hackers may pass through the security and steal data from the V2V (Vehicle to Vehicle) and V2I (Vehicle to infrastructure) communication system. We do not know how secure this communication is, as it is only tested in a restricted scenario. There may be a chance of hacking the self-driving system and force the cars to follow certain wanted route programmed by the hackers. [45] Automakers must ensure the communications are all encrypted and secure. Network security is very important in this communication system.  Ethical and moral reasoning come into the picture while programming the self-driving software that decides what action the car takes in case of an unavoidable crash; whether the self-driving car will crash into another vehicle, potentially killing people inside; or smash into something else, potentially killing its own passengers or nearby pedestrians; the question that programmers of AI systems find difficult to answer [46]. Scientists and researchers are working on the development of more intelligent humanoid A.I which will control the car like a professional driver. At the initial stages, there should be different lanes for self-driving cars and normal cars. This could help communicate and navigate the autonomous vehicle more easily.  Human pedestrians and drivers may find it difficult to communicate with the self-driving cars on the road. They have to face the challenge of determining the intentions of the autonomous vehicle. Drive.ai is testing a solution to this problem that involves LED signs mounted on the outside of the vehicle, announcing status such as "going now, don't cross" vs. "waiting for you to cross" [47]. Automotive giant Mercedes- Benz has demonstrated this technology where self-driving cars can communicate with humans and pedestrians by projecting certain signs and pictures on the road. Several incidents arise with the successful mass implementation of self-driving cars. On 20th January 2016, a crash of Tesla with Autopilot occurred in the Hubei province of China, which marks China’s first accidental death due to the autonomous system [48]. However, it is not proved whether the autonomous mode was active or not at that time. The second fatal accident took place on May 7, 2016, in Williston, Florida in which a Tesla Model S with Autopilot mode, crashed into a trailer killing the occupant of the car [49]. There are also few incidents of backlash from the common people against the implementation of the self-driving car. In October 2018, a self-driving test vehicle of Waymo was attacked by an assailant who carried out his attacks by sharp objects [50]. In one more harrowing incident, a man threatened an autonomous vehicle with a backup driver with a revolver. After investigation, it was found that the man wanted to take revenge for an accident happened earlier in which a woman was killed by a self-driving car [51]. At least 21 attacks happened in 2018 in Chandler reported by the Arizona Republic, against Google Waymo cars [52]. The growth of self-driving cars threatens the people of joblessness [41].

VI. CONCLUSION Self-driving technology is unarguably a step forward in the future of mobility. It is believed that with some further improvements, the self-driving car will be ready for implementation on a large scale. It may take some time to fully implement the autonomous vehicles in everyday life, as the technology needs advanced research and development. The need for robust control elements and advanced technology opens a great opportunity for researches in this field. The future of the autonomous vehicle sounds promising.

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BIOGRAPHY Aniruddha Chaki has completed Bachelor of Technology in Electrical Engineering from Siliguri Institute of Technology, India in 2020. His areas of interest and research include Computer Science, Electrical Systems and Drives, Software development, Web development etc. He has completed his training with Capgemini Technology Services India Limited and about to join the company as Software Analyst.