# The key technology toward the self-driving car

Jianfeng Zhao (School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China)
Bodong Liang (School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China)
Qiuxia Chen (School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China)

ISSN: 2049-6427

Article publication date: 2 January 2018

49973

## Abstract

### Purpose

The successful and commercial use of self-driving/driverless/unmanned/automated car will make human life easier. The paper aims to discuss this issue.

### Design/methodology/approach

This paper reviews the key technology of a self-driving car. In this paper, the four key technologies in self-driving car, namely, car navigation system, path planning, environment perception and car control, are addressed and surveyed. The main research institutions and groups in different countries are summarized. Finally, the debates of self-driving car are discussed and the development trend of self-driving car is predicted.

### Findings

This paper analyzes the key technology of self-driving car and illuminates the state-of-art of the self-driving car.

### Originality/value

The main research contents and key technology have been introduced. The research progress as well as the research institution has been summarized.

## Citation

Zhao, J., Liang, B. and Chen, Q. (2018), "The key technology toward the self-driving car", International Journal of Intelligent Unmanned Systems, Vol. 6 No. 1, pp. 2-20. https://doi.org/10.1108/IJIUS-08-2017-0008

## Publisher

:

Emerald Publishing Limited

## 1. Introduction

Nowadays, even though vehicle driving assistive technology has been assembled in the premium cars on a large scale, the concept of the self-driving car has constantly appeared in various news and reports (Ross, 2014; Ackerman, 2016a; Harris, 2016; Hassler, 2017; Computerworld, 2012). However, due to few literatures (Van Arem, 2014; Ibanez-Guzman et al., 2012) reviewing the key technology of self-driving car, many problems are ambiguous, i.e., what is the progress of self-driving car? Is the large-scale commercial use of self-driving car helpful to the human society? To address the above problems, this paper investigates the key technology of self-driving car, discusses its implementation obstacles and summarizes the whole picture of the technology progress, which are expected to be helpful for the reader to understand the commercial use of the self-driving car.

Generally, the self-driving car (Berger, 2014; Berger and Dukaczewski, 2014; Walker et al., 2001; Thrun, 2010; Baruch, 2016; Barker et al., 2013; Litman, 2015; Levinson et al., 2011), also termed as the wheeled mobile robot, is a kind of intelligent car, which arrives at a destination based on the information obtained from automotive sensors, including the perception of the path environment, information of the route and car control. The main characteristic of self-driving car is transporting people or objects to a predetermined target without humans driving the car. According to the National Highway Traffic Safety Administration, the self-driving car can be classified into four levels, as described in Table I. Due to the maturity of Levels 1 and 2, this paper discusses Levels 3 and 4.

## 2. The key technology of a self-driving car

The automatic control, architecture, artificial intelligence, computer vision and many other technologies are integrated into the self-driving car, which is a product of the highly developed computer science, pattern recognition and intelligent control technology. From a different viewpoint, the technology of self-driving car represents the level of scientific research and industrial strength of a country. However, few papers have surveyed the technology process of a self-driving car due to its complexity. In view of this problem, this paper proposed a new classification, as shown in Figure 1, for the key technology of self-driving car according to the function implementation, which will make the description easy and clear.

Compared with manual driving, it is the key characteristic of a self-driving car that using automation equipment to replace the human driver. Based on this characteristic and functional requirement on driving and on-board equipment module, the core technology of self-driving car is classified into four key parts, which are known as car navigation system, path planning, environment perception and car control. The detailed description is provided in the following sections.

Compared with the classification method according to automotive level, this paper proposes a new classification according to the function realization of the self-driving car. This classification is able to clearly express the technical requirements of a self-driving car, helping the researchers and relevant enterprises to understand the technical implementation of self-diving car; meanwhile, it is able to clearly describe the key technologies of implementing the self-driving car and its latest progress.

From the view of classification, this paper divides the key technology of self-driving car into four parts according to the function of a self-driving car: environment perception, car navigation, path planning, and the car control. Each part is independent with others no overlapping coverage. This classification is inspired by the operation steps of human driving vehicles and is easy for researchers to understand.

During self-driving, two issues, which are the current location of the car and how to go from the location to the destination, must be resolved. Certainly, the above two issues can be solved by a human’s own knowledge in human driving. However, in self-driving, the car must be able to automatically and intelligently locate its position and perform the path planning to destination. For this objective, the on-board car navigation system is deployed on the self-driving car.

The structure of car navigation system and its metadata processing model are depicted in Figure 2. In the car navigation system, geographic information system and global positioning system (GPS) are equipped to receive the location information such as longitude and latitude from the satellite. These information, together with the road information generated by location system and digital map database, serve as the source data inputted into the map-matching model, where the intelligent path planning algorithms (i.e. Dijkstra algorithm, Bellman-Ford algorithm) are utilized to enable the path planning calculation. After calculation, the self-driving car can locate itself. With the information of the self-driving car’s location and the destination, the driving route can also be programmed and calculated by the path planning model.

### 2.2 Location system

The main purpose of the location system is to determine the vehicle location, which generally can be classified into relative location, absolute location and hybrid location. For relative location, the current position of self-driving car is obtained by adding the moving distance and direction to the prior position. For instance, inertial navigation system (INS) (Farrell and Barth, 1999) is a common relative location system. In INS, the vehicle angular velocity and accelerated velocity are obtained by the gyroscope sensor and accelerometer installed in the car. By integrating these data (i.e. angular velocity, accelerated velocity), the car’s relative course angle and speed can be calculated. Similarly, the car’s direction and mileage can be obtained by integrating the course angle and speed once again. Combining with the prior vehicle location, the current vehicle location can be calculated. However, due to the vehicle vibration during moving, it is inevitable to lead to the deviation between the calculated location and actual location.

The absolute location method is used to locate the vehicle’s position according to the information obtained from positioning system. A common positioning system is the satellite-based system, such as GPS, GLONASS, Galileo, Beidou and so on. However, the satellite signal is prone to the interference from the weather conditions and urban environment, such as building and mountain, which will cause error and noise in the location signal, and thus the measured absolute location is not accurate.

The hybrid location, which combines the characteristics of the above two locating methods, is the most common method used in obtaining the position of a self-driving car. For instance, the self-driving car of Shaihai Jiaotong University involves a typical hybrid location implementation system, which implements the Gmouse UB-353 USB GPS model and Analog Device ADIS16300 INS (Yida, 2013) to obtain information of the location.

GPS/INS can be not only used for navigation, but also for location applications, such as turning. For instance, Zhu et al. (2012) proposed a new vehicle cross-road turning method based on the GPS/INS information. According to this method, the vehicle turning can be achieved by adopting a predefined map, which is generated by the line curve-fitting and predicting method based on the location and road condition given by GPS/INS. Carnegie Mellon University (Urmson and Whittaker, 2017) made use of sparse GPS data combined with the aerial imagery to locate the self-diving car in the road, which was named Boss.

The major GPS/inertial measurement unit (IMU) manufacturers are as follows: NovAtel, Leica, CSI Wireless and Thales Navigation, etc. NovAtel proposed the SPAN technology. SPAN combines the GPS location with absolute accuracy with the IMU gyro and accelerometer measurements stability to provide a solution with 3D position, velocity and attitude. Even when the GPS signal is blocked, it can provide a stable and continuous solution. Based on the SPAN technology, NovAtel has two major GPS/IMU products: SPAN-CPT integrated navigation system and SPAN-FSAS fractional navigation system. SPAN-CPT uses a Novatel professional high-definition GPS board card and the German iMAR company’s fiber optic gyro IMU. Its solution accuracy can be applied to different positioning requirements in different modes, including SBAs, L-band (Omnistar and CDGPS) and RTK difference and so on. This system has the highest course accuracy of 0.05°, and the pitch rolling accuracy is 0.015°. SPAN-FSAS also uses the German IMAR company high-definition (HD) close-loop technology IMU, and the gyro deviation obtained is less than 0.75 degree/hour and the accelerometer deviation is less than 1 mg. By combining it with NovAtel FlexPak6™ or ProPak6™, the combination navigation solution can be achieved. The output speed of the GNSS+INS system is up to 200 Hz while IMU-FSAS sending inertial measurement data to GNSS receiver.

### 2.3 Electronic map (EM)

EM is used for digital map information storage, which mainly includes geographical characteristics, traffic information, building information, traffic signs, road facilities, etc. Nowadays, most of the EMs which are used in a self-driving car are the EMs designed for humans. It is expected that special EMs for self-driving, such as automatic road sign recognition, car’s driving information interacting among self-driving cars, will be developed in the future.

Now, the EM for self-driving car named HD map has already shown up. Compared with the traditional map, on the one hand, the accuracy of absolute coordinates of an HD map is higher. For example, it is declared that its next generation of drawing applications will be accurate in centimeters and, on the other hand, the road traffic information elements are richer and more detailed. In particular, the HD map is divided into three layers: the active layer, the dynamic layer and the analytical layer:

1. Active layer, compared to the traditional map, adds HD road-level data (road shape, slope, curvature, laying, direction, etc.), the data of lane attribute (lane type, lane width, etc.) and the elevated objects, guardrail, trees, road edge types, roadside landmarks and other large target data.

2. Dynamic layer will update real-time traffic data from other vehicle sensors and road sensors. The update and supplement is in real time. This is the second phase of HD map, namely, network integration-collaborative perception.

3. Analysis layer helps train self-driving car by analyzing the real-time big data of human driving records. Therefore, the HD map enters the third phase of network integration-coordinated decision-making and control.

At present, the ADAS map has the activity layer information and the accuracy is 1-5 m. For example, BMW Adaptive Speed Recommendation (ASR) will remind users to slow down ahead of 50-300 meters in case of a slowdown area, the concrete meters will adjust depending on the current speed, the braking speed and the time of driver responding time will adjust; at the turn of the road, ASR will consider the road width, the number of lanes, the whole road condition, etc. to calculate the reasonable speed of the car.

Current HD map is of an ADAS level, which can be applied to L2/L3 self-driving. In the future, by incorporating the data processing facilities of internet of car by bringing 5 G, taking into account the nature of computer vision, considering 3D modeling technology, the development of cloud computing technology based on the deep-learning environment perception and end-closed loop real-time update, HD map will gradually have highly automated driving level. This paper expects that the HD map will gradually mature with the 5 G standard establishment and with the artificial intelligence eruption entering the mature stage, and become one of key technologies to support intelligent driving network.

### 2.4 Map matching

Map matching, which is the foundation of the path planning, calculates out the car’s location by using the geographical information from GPS/INS and the map information from EM. During the calculation, the advanced fusing technique is employed to fuse the longitude and attitude or other coordinates information into the EM. From the practical viewpoint, the output of car location should be accurate and time efficient. In this regard, it is an important issue to find a good method to fuse the information from GPS and INS. In fact, sometimes the satellite signal in GPS or the INS could be lost, therefore, a good data fusion method that can integrate the information from the existing location and route scenario will greatly enhance the accuracy, robustness and reliability.

Therefore, it is the research hotspot to make use of vehicle running characteristics in map matching, for example, those literatures proposed a novel method to solve map matching (Liu et al., 2017; Rohani et al., 2016; Zeng et al., 2016). Besides, hidden Markov model (HMM) and heuristic algorithms are some competitive algorithms in those methods, for example, the literature (Mohamed et al., 2017) presents a new method named SnapNet, which provides accurate real-time map matching for a cellular-based trajectory trace and employs a novel incremental HMM algorithm to solve the problem. In the paper of Jagadeesh and Srikanthan (2017), a novel map-matching solution is proposed which combines the widely used approach based on a HMM with the concept of drivers’ route choice. The similar articles using HMM include: Atia et al. (2017), Zhou et al. (2017) and Wang and Zimmermann (2014) and so on. We argue that there will be more and more heuristic algorithms for map matching, for example, the literature (Gong et al., 2017) develops a novel map-matching model that considers local geometric/topological information and a global similarity measure simultaneously and adopts the ant colony algorithm to accomplish the optimization goal in this complex model.

### 2.5 Global Path Planning

Global Path Planning is used to determine the optimal driving path between the start point and end point. Generally, the typical path planning algorithms, such as Dijkstra algorithm, Bellman-Ford algorithm, Floyd algorithm and heuristic algorithm (Seshan and Maitra, 2014) are employed to fuse the EM information and calculate the optimal path. Due to the global path, planning is at mature stage and already implemented commercially on a large scale, so this paper will not cover this topic.

### 2.6 The next step of navigation system

In path planning, the module of location is required to integrate the information from EM. Even though the key technology of location (i.e. location system and the EM) in self-driving car has been matured and implemented at the commercial level, there are still many challenges that we have to face in the future:

1. The tradeoff between the cost and the accuracy: the current location system in a self-driving car depends mainly on the satellite location system; however, to achieve the stable and accuracy of satellite signal, high-accuracy location information extraction is required, and then high cost is required to spend on the additional equipment. Therefore, it is necessary to reduce the cost in the future large-scale commercial use, while at the same time maintaining the accuracy of location.

2. The tradeoff between the location accuracy and speed: It is necessary to accurately locate the self-driving car even in high-speed moving scenario; however, higher speed leads to fast update of the location information, and more information is required to be integrated. However, due to the limited computation ability and processing speed (i.e. CPU) of the equipment, the in-time calculation of location information cannot be achieved, and thus it leads to the inaccuracy of location. Therefore, obtaining high-accuracy location under high-speed condition is a future research direction.

3. The special EM for self-driving car: in recent times, the general EM is utilized in self-driving, while it is necessary to develop a special EM for a self-driving car to consider the human identity, i.e. hobby of human, profession of human, which can reduce the response time of EM.

### 2.7 Environment perception

Environment perception is the second module of a self-driving car. To provide necessary information for a car’s control decision, the car is required to independently perceive surrounding environment. The major methods of environment perception include laser navigation, visual navigation and radar navigation.

During environment perception, multi-sensors (i.e. laser sensor, radar sensor) are deployed to sense the comprehensive information from the environment, which are then fused to perceive the environment. Among the sensors, the laser sensor is utilized for bridging between the real world and data world, radar sensor is used for distance perception and visual sensor is for traffic sign recognition. A typical recognition scheme is shown in Figure 3, the self-driving car fuses data from laser sensors, radar sensors and visual sensors, and generates the surrounding environment perception, such as road edge stone, obstacles, road marking and so on.

### 2.8 Laser perception

Strictly speaking, laser perception system is a kind of radar system. In laser perception, a continuous laser or laser pulse is launched to the target, and a reflected signal is received at the transmitter. By measuring the reflection time, reflection signal strength and the shift of the operation frequency, the cloud data of target point can be generated, then the testing object information, such as location (distance and angle), shape (size) and state (velocity and attitude) can be calculated out.

Laser sensor is the main sensor in environment perception (Ackerman, 2016b). According to the dimension of sensed information, laser sensor can be classified into single-line laser radar, multi-line laser radar and three-dimensional omnidirectional laser radar (Fei, 2012). These lasers usually work in complicated outdoor environment. Different kinds of laser radar have their defects. For instance, false detection usually occurs in a single-line laser radar due to less information sensed by the single-line radar; owing to the asymmetry information from different line in multiple-line laser radar, the accuracy of output is limited and insufficient, and the vision range is smaller due to the smaller inter-section region among the lines; due to the large amount of data generated in a three-dimensional omnidirectional laser radar, it is difficult for the algorithm to generate the real-time output. Therefore, in the complex outdoor environment, especially the moving of the cars and human, it is challenging to reasonably configure different kinds of sensors to achieve the location of a moving obstacle, and improve the output performance in terms of vision, accuracy and real time.

### 4.2 Social habit

Social habit is a very important issue in sociological research. As the level of self-driving matures, it will have a great impact on people’s transportation. First of all, the taxi and the truck will be replaced, it is difficult to effectively reduce the impact on various industries brought by the technological progress. The second one is the impact on public transport. The self-driving car may be more convenient for the people to travel, on the other hand, it is possible to collapse the existing social transport model, leading to less bus services and more congested urban traffic. Finally, the self-driving cars may bring different perceptions of wealth. Today, sharing economy is growing in the world, in the future, the self-driving car must promote the development of sharing economic in the field of road traffic, then human beings will be more willing to share in the future. Some typical literatures, such as Richards and Stedmon (2016), Banks and Stanton (2016), Banks et al. (2014), Brooks (2017), Conejero et al. (2016), Yang and Coughlin (2014), Surden and Williams (2016), etc. discuss these issues.

### 4.3 Human psychology

The problem stems from two aspects, one is the human demand for security, and the other is the social and ethical issues. For more than one century, people have been accustomed to the control of vehicles. Different from other new things, the self-driving car is more likely to cause passengers injuries or even death. It has a great impact on the human psychology. Many people are not willing to use self-driving car in order to achieve more psychological security, while few people do not use self-driving because of their love for driving. Therefore, for a long time, it will be the era of co-existence of both self-driving car and human driving car. In terms of social ethics, how to choose between passenger safety and pedestrian safety when they are in danger? When the danger occurs, how to choose a young child and an old man? How to make an emergency safe haven is always a human psychological problem. This issue has also aroused the attention of scholars (Kirkpatrick, 2015; Baruch, 2016; Diels and Bos, 2016; Li et al., 2016; Lee et al., 2015).

### 4.4 Law problem

The current legal system already cannot meet the self-driving car. There are four problems: first is the license problem. At present, many countries do not make the rules for the self-driving cars. There are no countries or regions that give self-driving car license, only California and some American states issued a test permit and with the progress of the self-driving car, whether it is legal that the existing vehicles be equipped with self-driving control system?; Second is driving regulations. Whether the driving regulations of self-driving cars are determined according to the requirements of human driving, it is also an issue in the current legal profession. Third is the definition of responsibility. How to define the responsibility? Whether there should be someone sitting in the driver’s seat, whether the passengers sitting in the driving position should have the driving skills, whether the passengers should bear the corresponding responsibility, all those are the legal problems of self-driving. Fourth is the information security. Whether the self-driving car has the right to record the path of passage? Is the mapping of self-driving car related to information security in a country or region?

In any case, law problem is going forward with dispute (Greenblatt, 2016). The Vienna Convention for Road Traffic, which is on the road traffic management, was amended in the United Nations on March 23, 2016. It removes the obstacle for applying a self-driving car in the transportation. The 1958 Agreement, developed by the United Nations Coordination Forum on World Vehicle Regulations, proposes to remove the speed limit for the active steering function application, which is expected to be discussed in 2017. The USA is doing its best in the self-driving car legal framework, not only some states have enacted relevant test bills and developed rules, but also the federal formulates the related rules and laws: Federal Automated Vehicles Policy (USDO Transportation, 2016), Safely Ensuring Lives Future Deployment and Research in Vehicle Evolution Act (US Congress, 2017). The other countries are only enacting some testing allowable terms and are considering the relevant legislation.

Nowadays, more and more driving assistance technologies originated from the self-driving car have been utilized in the traditional car. It can be predicted that the realization of self-driving car will gradually develop from the assistance driving to the self-driving in special environment (such as highway), and finally to the total self-driving. In recent times, many driving assistance technologies such as lane keeping assist, adaptive cruise control and so on, have been commercialized. In the near future, the commercial self-driving car under supervision in some special sections will be developed, such as the car will self-drive in a highway, which will be the milestone of self-driving. In the future, full self-driving car will be accepted as a common driving pattern.

## Figures

#### Figure 1

A classification of the key technology for self-driving

#### Figure 3

A typical perception scheme of self-driving car

#### Figure 4

SLAM schematic diagram

#### Figure 5

The position of vehicle control in self-driving car Vehicle Control

#### Figure 6

The principle of PID algorithm

## Table I

The classification of vehicle automation by the National Highway Traffic Safety Administration (NHTSA)

Level Judgment standard
No-automation (Level 0) The driver completely controls the vehicle all the time
Function-specific automation (Level 1) Individual vehicle controls are automated, such as electronic stability control or automatic braking
Combined function automation (Level 2) At least two controls can be automated in unison, such as adaptive cruise control in combination with lane keeping
Limited self-driving automation (Level 3) The driver can fully cede control of all safety-critical functions in certain conditions. The car senses when conditions require the driver to retake control and provides a “sufficiently comfortable transition time” for the driver to do so
Full self-driving automation (Level 4) The vehicle performs all safety-critical functions for the entire trip, with the driver not expected to control the vehicle at any time. As this vehicle would control all functions from start to stop, including all parking functions, it could include unoccupied cars

## Table II

The DARPA Grand Challenge Champions

Year Vehicle Team name Team home
2004 Sandstorm Red Team Carnegie Mellon University, Pittsburgh, Pennsylvania
2005 Stanley Stanford Racing Team Stanford University, Palo Alto, California
2007 Boss Tartan Racing Carnegie Mellon University, Pittsburgh, Pennsylvania

## Table III

The outstanding participants in self-driving related project of ELROB over the year

Year Project Team/source
2007 Urban scenario: situation awareness in urban environment University of Würzburg
Telerob
University of Hannover
2009 Transport – Mule (non-urban) University of Hannover
University of Kaiserslautern
Robotics Inventions
2011 Transport – Mule Fraunhofer FKIE
University of Hannover
2013 Autonomous navigation using GPS, GLONASS and Galileo MuCAR/University of the Bundeswehr Munich
RIS/LAAS/CNRS
NAMT/Nizhny Novgorod Automotive Technical School (NAMT)

## Table IV

The best player in future challenge over the year

Year Team From
2009 Self-driving car Hunan University
2010 Intelligent Pioneer Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science Chinese Academy of Science and Chery Central Research Institute
2011 Explore Lion National University of Defense Technology
2012 Brave Lion 3 Military Transportation University
2013 Smart 2 Beijing Institute of Technology and BYD Corporation
2014 Junjiao Lion Military Transportation University

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## Acknowledgements

The authors would like to thank the financial support from Shenzhen Science and Technology Innovation Committee (Nos JCYJ20160429145314252, JCYJ20160527162817715, JCYJ20160407160609492), Guangdong Provincial Science and Technology Plan projects (No. 2016A010101039), Shenzhen Polytechnic (Nos 601522k30007, 601522K30015).

## Corresponding author

Dr Bodong Liang is the corresponding author and can be contacted at: liangbodong@szpt.edu.cn