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ZHAO Xiang-mo's team supported by the National Key Research and Development Program of China (2021YFB2501200). Research progress in testing and evaluation technologies for autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 10-77. doi: 10.19818/j.cnki.1671-1637.2023.06.002
Citation: ZHAO Xiang-mo's team supported by the National Key Research and Development Program of China (2021YFB2501200). Research progress in testing and evaluation technologies for autonomous driving[J]. Journal of Traffic and Transportation Engineering, 2023, 23(6): 10-77. doi: 10.19818/j.cnki.1671-1637.2023.06.002
shu

Research progress in testing and evaluation technologies for autonomous driving

doi: 10.19818/j.cnki.1671-1637.2023.06.002
Funds:

National Key Research and Development Program of China 2021YFB2501200

  • Received Date: 2023-06-19
  • Publish Date: 2023-12-25
    Abstract
  • In view of the high cost, long cycle, low coverage, and lack of a perfect tool chain for autonomous driving vehicle tests in the actual complex traffic environment, the research status of seven major areas of testing and evaluation technologies for autonomous driving was analyzed, and the future development direction of testing and evaluation technologies for autonomous driving was predicted, including simulation and testing technology of autonomous driving vehicles, simulation and testing technology of traffic flow, hardware-in-the-loop testing technology, field testing technology, intelligence evaluation technology, testing and evaluation tool chain and its system's construction, certification and potential defect detection technology, etc. In terms of simulation and testing of autonomous driving vehicles, the research status of simulation and testing software for autonomous driving, vehicle dynamics models, background vehicle interaction behavior models for testing, simulation and testing of cloud control platform supervision, and standardization of vehicle simulation system was analyzed. The main problems currently existing in the simulation and testing of autonomous driving vehicles were summarized. In terms of simulation and testing of autonomous driving traffic flow, the research status of driving style models for test background vehicles, traffic flow modeling and simulation, traffic scenario generation methods, and acceleration testing methods was summarized, and the future development trend of simulation and testing of autonomous driving traffic flow was predicted. In terms of hardware-in-the-loop testing technology, the human-vehicle-road-loop multi-dimensional digital twin tests and construction methods of the system platform for autonomous driving vehicles were summarized. Typical sensor data from high-definition cameras, millimeter-wave radars, and ultrasonic radars, as well as simulation technology of vehicle-to-vehicle and vehicle-to-road communication signals, were reviewed. In terms of field testing technology, the development status of closed field testing, open road testing, and highway test-related testing fields, testing standards, and key technologies were summarized. In terms of intelligence evaluation technology, the research status of intelligence evaluation methods for autonomous driving was introduced from four aspects: the concept of autonomous driving intelligence, the quantification and evaluation of scene complexity, the intelligence evaluation systems of autonomous driving, and the social cooperation evaluation methods. In terms of testing and evaluation tool chain and system construction, the current situation of the testing and evaluation standard system for autonomous driving was introduced mainly from three aspects: the testing and evaluation tool chain technology, the autonomous driving testing evaluation system, and the current situation of autonomous driving testing standards. Finally, in terms of certification and potential defect detection technology, the current defect detection methods for autonomous driving were reviewed from the definition, cause, classification, and detection of autonomous driving defects. The challenges faced in the safety assurance of autonomous driving vehicles were summarized. Research results show that although the autonomous driving testing and evaluation technology has made great progress, the testing and evaluation standard system is still not perfect, and the existing testing tools and methods fail to meet the testing needs of autonomous driving vehicles at level three and above. The development and application level of virtual simulation and digital twin technology is low, and there are many deficiencies in the degree of simulation, testing efficiency, and vehicle testing ability. In the future, it is necessary to further strengthen the research and development of full-scene and high-fidelity modeling technology and real-time simulation software, establish an online accelerated twin testing system with virtual and real interaction, study the scenario generation and acceleration methods of autonomous driving full-stack hazardous testing, and integrate autonomous driving testing technologies and tools, so as to form a tool chain for autonomous driving testing and evaluation and improve standard specifications.

     

    • Author resume: ZHAO Xiang-mo(1966-), male, professor, PhD, xmzhao@chd.edu.cn
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