Automatic Virtual Test Technology for Intelligent Driving Systems Considering Both Coverage and Efficiency

被引:44
|
作者
Gao, Feng [1 ,2 ]
Duan, Jianli [3 ]
Han, Zaidao [1 ]
He, Yingdong [4 ]
机构
[1] Chongqing Univ, Sch Automot Engn, Chongqing 400044, Peoples R China
[2] Shanghai Jiao Tong Univ, Sichuan Res Inst, Chengdu 610200, Peoples R China
[3] Tsinghua Univ, Sch Vehicle & Mobil, Beijing 100000, Peoples R China
[4] Univ Michigan, Mech Engn, Ann Arbor, MI 48109 USA
关键词
Testing; Mathematical model; Complexity theory; Analytical models; Solid modeling; Matlab; Three-dimensional displays; Autonomous vehicles; intelligent driving systems; model-in-the-loop testing; automatic test and evaluation; combinational testing;
D O I
10.1109/TVT.2020.3033565
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The testing of the intelligent driving systems is faced with the challenges of efficiency because real traffic scenarios are infinite, uncontrollable and difficult to be precisely defined. Based on the complexity index of scenario that designed to measure the test effect indirectly, a new combinational testing algorithm of test cases generation is proposed to make a balance among multiple objects including test coverage, the number of test cases and test effect. Then a joint simulation platform based on Matlab, PreScan and Carsim is built up to realize the construction of 3D test environment, execution of test scenarios and evaluation of test results automatically and seamlessly. The strategy proposed in this paper is validated by applying it to a traffic jam pilot system. The result shows that the proposed strategy can improve the overall complexity of the designed test scenarios effectively, which can help us detect system faults faster and easier. And the time required to conduct tests is reduced obviously by means of automation.
引用
收藏
页码:14365 / 14376
页数:12
相关论文
共 42 条
  • [1] A Test Scenario Automatic Generation Strategy for Intelligent Driving Systems
    Gao, Feng
    Duan, Jianli
    He, Yingdong
    Wang, Zilong
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2019, 2019
  • [2] Test Scenario Generation and Optimization Technology for Intelligent Driving Systems
    Duan, Jianli
    Gao, Feng
    He, Yingdong
    IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 2022, 14 (01) : 115 - 127
  • [3] ISTA: Automatic Test Case Generation and Optimization for Intelligent Systems based on Coverage Analysis
    Zheng, Wei
    Lin, Lidan
    Chen, Xiang
    Liu, Guoliang
    Huang, Hao
    Shen, Jinjin
    Xu, Qingqing
    Gu, Yizeng
    2023 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING, SANER, 2023, : 758 - 762
  • [4] Test Scenario Design for Intelligent Driving System Ensuring Coverage and Effectiveness
    Qin Xia
    Jianli Duan
    Feng Gao
    Qiuxia Hu
    Yingdong He
    International Journal of Automotive Technology, 2018, 19 : 751 - 758
  • [5] Test Scenario Design for Intelligent Driving System Ensuring Coverage and Effectiveness
    Xia, Qin
    Duan, Jianli
    Gao, Feng
    Hu, Qiuxia
    He, Yingdong
    INTERNATIONAL JOURNAL OF AUTOMOTIVE TECHNOLOGY, 2018, 19 (04) : 751 - 758
  • [6] Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving
    Ding, Zirui
    Xiang, Junping
    WORLD ELECTRIC VEHICLE JOURNAL, 2021, 12 (04)
  • [7] Autonomous-driving vehicle test technology based on virtual reality
    Yao, Shouwen
    Zhang, Jiahao
    Hu, Ziran
    Wang, Yu
    Zhou, Xilin
    JOURNAL OF ENGINEERING-JOE, 2018, (16): : 1768 - 1771
  • [8] Research on Automatic Test Technology for Field Operation and Maintenance of Intelligent Substation
    Zhou, Di
    Wang, Zizhan
    Zhou, You
    Mao, Yurong
    Zhou, Minqi
    2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2020), 2020, : 11 - 15
  • [9] The Use of Virtual Reality Technology in Intelligent Transportation Systems Education
    Pense, Caner
    Tektas, Mehmet
    Kanj, Hassan
    Ali, Nawaf
    SUSTAINABILITY, 2023, 15 (01)
  • [10] Research on a Simulation Method of the Millimeter Wave Radar Virtual Test Environment for Intelligent Driving
    Li, Xin
    Tao, Xiaowen
    Zhu, Bing
    Deng, Weiwen
    SENSORS, 2020, 20 (07)