自动驾驶仿真测试场景生成技术研究进展

doi:10.3778/j.issn.1002-8331.2405-0117

摘要/Abstract

摘要： 自动驾驶系统已经成为汽车行业和计算机科学的前沿研究领域。为了确保自动驾驶系统的安全性和可靠性，对自动驾驶系统进行充分的测试是十分必要的，而仿真测试因其测试成本低、安全性高等优点，并且测试场景是自动驾驶仿真测试的关键，因此仿真测试场景生成是自动驾驶测试过程中不可或缺的环节。目前，已有大量学者致力于研究自动驾驶仿真测试，也有许多关于自动驾驶仿真测试的研究进展报告，但是针对于自动驾驶仿真测试场景生成的研究进展报告却寥寥无几。因此,对关于自动驾驶仿真测试场景的文献进行了全面调查，并对自动驾驶仿真测试的相关背景知识进行概述；通过调研国内外几十篇相关文献，按照自动驾驶系统的模块进行归纳总结，对现有仿真测试场景生成方法进行详细分类阐述；通过调研目前主流的仿真测试工具，对现有仿真测试工具进行分析归纳；分析了自动驾驶仿真测试领域面临的挑战和未来展望。

关键词: 自动驾驶, 仿真测试, 测试场景, 整车测试, 仿真测试工具

Abstract: Autonomous driving systems have emerged as a cutting-edge research domain at the intersection of the automotive industry and computer science. Ensuring the safety and reliability of autonomous driving systems necessitates comprehensive testing. Simulation testing, characterized by its low cost and high safety, plays a pivotal role in this context. The generation of simulation test scenarios is an indispensable component in the autonomous driving testing process. To date, numerous scholars have dedicated their efforts to the study of autonomous driving simulation testing, and there have been numerous reports on the advancements in this field. However, research progress reports specifically addressing the generation of simulation test scenarios for autonomous driving are scarce. Therefore, this paper commences with a comprehensive literature review on autonomous driving simulation test scenarios and provides an overview of the related background knowledge. Subsequently, it systematically categorizes and elaborates on the existing methods for generating simulation test scenarios by investigating dozens of relevant domestic and international literature, organized according to the modules of the autonomous driving system. Furthermore, the paper analyzes and summarizes the current mainstream simulation testing tools. Finally, the paper discusses the challenges and future prospects faced in the field of autonomous driving simulation testing.

Key words: autonomous driving, simulation testing, test scenario, vehicle testing, simulation testing tool

孙乐乐, 黄松, 郑长友, 夏春艳, 阳真. 自动驾驶仿真测试场景生成技术研究进展[J]. 计算机工程与应用, 2025, 61(1): 59-79.

SUN Lele, HUANG Song, ZHENG Changyou, XIA Chunyan, YANG Zhen. Research Progress on Autonomous Driving Simulation Test Scenario Generation Technology[J]. Computer Engineering and Applications, 2025, 61(1): 59-79.

参考文献

[1] Baidu. Baidu apollo github repository[EB/OL].[2024-03-20]. https://apollo.baidu.com/.
[2] National Highway Traffic Safety Administration (NHTSA). Standing general order on crash reporting[EB/OL]. [2024-03-20]. https://www.nhtsa.gov/laws-regulations/standing-general-order-crash-reporting#data.
[3] 蒋拯民, 党少博, 李慧云, 等. 自动驾驶汽车场景测试研究进展综述[J]. 汽车技术，2022(8): 10-22.
JIANG Z M, DANG S B, LI H Y, et al. A survey on the research progress of scenario-based testing for autonomous vehicles[J]. Automobile Technology, 2022(8): 10-22.
[4] LG. Lgsvl simulator[EB/OL]. [2024-03-20]. https://www. svlsimulator. com/.
[5] Carla. Open urban driving simulator[EB/OL]. [2024-03-20]. http://carla.org/.
[6] 朱向雷, 王海弛, 尤翰墨, 等. 自动驾驶智能系统测试研究综述[J]. 软件学报, 2021, 32(7): 2056-2077.
ZHU X L, WANG H C, YOU H M, et al. Survey on testing of intelligent systems in autonomous vehicles[J]. Journal of Software, 2021, 32(7): 2056-2077.
[7] TANG S, ZHANG Z, ZHANG Y, et al. A survey on automated driving system testing: landscapes and trends[J]. ACM Transactions on Software Engineering and Methodology, 2023, 32(5): 1-62.
[8] DAFTRY S, ZENG S, BAGNELL J A, et al. Introspective perception: learning to predict failures in vision systems[C]//Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016: 1743-1750.
[9] LU J, SIBAI H, FABRY E, et al. No need to worry about adversarial examples in object detection in autonomous vehicles[J]. arXiv:1707.03501, 2017.
[10] GURAU C, RAO D, TONG C H, et al. Learn from experience: probabilistic prediction of perception performance to avoid failure[J]. The International Journal of Robotics Research, 2018, 37(9): 981-995.
[11] EYKHOLT K, EVTIMOV I, FERNANDES E, et al. Note on attacking object detectors with adversarial stickers[J]. arXiv:1712.08062, 2017.
[12] XIONG C, ZHANG Z, ZHOU Y, et al. IEMT: inequality-based metamorphic testing for autonomous driving models[C]//Proceedings of the 22nd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2022-Companion, Guangzhou, China, 2022: 283-287.
[13] CORDTS M, OMRAN M, RAMOS S, et al. The cityscapes dataset for semantic urban scene understanding[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 3213-3223.
[14] Udacity. self-driving-car/datasets[EB/OL].[2024-03-20]. https://github.com/udacity/self-driving-car/tree/master/datasets/CH2.
[15] GEYER J, KASSAHUN Y, MAHMUDI M, et al. A2D2: audi autonomous driving dataset[J]. arXiv:2004.06320, 2020.
[16] DREOSSI T, GHOSH S, SANGIOVANNI-VINCENTELLI A L, et al. Systematic testing of convolutional neural networks for autonomous driving[J]. arXiv:1708.03309, 2017.
[17] RAMANAGOPAL M S, ANDERSON C, VASUDEVAN R, et al. Failing to learn: autonomously identifying perception failures for self-driving cars[J]. IEEE Robotics & Automation Letters, 2018, 3(4): 3860-3867.
[18] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231-1237.
[19] ZHU Y, MIAO C, HAJIAGHAJANI F, et al. Adversarial attacks against LiDAR semantic segmentation in autonomous driving[C]//Proceedings of the ACM International Conference on Embedded Networked Sensor Systems, 2021: 329-342.
[20] BEHLEY J, GARBADE M, MILIOTO A, et al. Semantic-KITTI: a dataset for semantic scene understanding of LiDAR sequences[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019: 9296-9306.
[21] YANG Z, HUANG S, ZHENG C, et al. MetaLiDAR: automated metamorphic testing of LiDAR‐based autonomous driving systems[J]. Journal of Software: Evolution and Process, 2023: e2644.
[22] ALHAIJA H A, MUSTIKOVELA S K, MESCHEDER L M, et al. Augmented reality meets computer vision: efficient data generation for urban driving scenes[J]. arXiv:1708. 01566, 2017.
[23] GAIDON A, WANG Q, CABON Y, et al. Virtual worlds as proxy for multi-object tracking analysis[J]. arXiv:1605.06457, 2016.
[24] WANG J, PUN A, TU J, et al. AdvSim: generating safety-critical scenarios for self-driving vehicles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021: 9909-9918.
[25] WANG J, WANG X, SHEN T, et al. Parallel vision for long-tail regularization: initial results from IVFC autonomous driving testing[J]. IEEE Transactions on Intelligent Vehicles, 2022, 7(2): 286-299.
[26] VISHNU C, KHANDELWAL J, MOHAN C K, et al. EVAA-exchange vanishing adversarial attack on LiDAR point clouds in autonomous vehicles[J]. IEEE Trans Geosci Remote Sens, 2023, 61: 1-10.
[27] CAO Y, XIAO C, CYR B, et al. Adversarial sensor attack on lidar-based perception in autonomous driving[J]. arXiv:1907.06826, 2019.
[28] LEHNER A, GASPERINI S, MARCOS-RAMIRO A, et al. 3D-VField: adversarial augmentation of point clouds for domain generalization in 3D object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 2022: 17274-17283.
[29] ZHAO Y, ZHU H, LIANG R, et al. Seeing isn’t believing: towards more robust adversarial attack against real world object detectors[C]//Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, London, UK, 2019: 1989-2004.
[30] CHOI J I, TIAN Q. Adversarial attack and defense of YOLO detectors in autonomous driving scenarios[C]//Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, Aachen, Germany, 2022: 1011-1017.
[31] WOODLIEF T, ELBAUM S G, SULLIVAN K. Semantic image fuzzing of AI perception systems[C]//Proceedings of the 44th IEEE/ACM 44th International Conference on Software Engineering, Pittsburgh, PA, USA, 2022: 1958-1969.
[32] LAURENT T, ARCAINI P, ISHIKAWA F, et al. A mutation-based approach for assessing weight coverage of a path planner[C]//Proceedings of the 2019 26th Asia-Pacific Software Engineering Conference (APSEC), 2019: 94-101.
[33] BAK S, BETZ J, CHAWLA A, et al. Stress testing autonomous racing overtake maneuvers with RRT[C]//Proceedings of the 2022 IEEE Intelligent Vehicles Symposium, Aachen, Germany, 2022: 806-812.
[34] KAHN M, SARKAR A, CZARNECKI K. I know you can’t see me: dynamic occlusion-aware safety validation of strategic planners for autonomous vehicles using hypergames[C]//Proceedings of the 2022 International Conference on Robotics and Automation, Philadelphia, PA, USA, 2022: 11202-11208.
[35] HUSSAIN M, ALI N, HONG J E. DeepGuard: a framework for safeguarding autonomous driving systems from inconsistent behaviour[J]. Automated Software Engineering, 2021, 29(1): 1.
[36] TUNCALI C E, PAVLIC T P, FAINEKOS G. Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles[C]//Proceedings of the 19th IEEE International Conference on Intelligent Transportation Systems, Rio de Janeiro, Brazil, 2016: 1470-1475.
[37] CarSim. Mechanical simulation corporation[EB/OL]. [2024-03-20]. https://www.carsim.com/.
[38] MICHEL O. WebotsTM: Professional Mobile Robot Simulation[J]. arXiv:cs/0412052, 2004.
[39] MATLAB. Programming and numerical computation platform[EB/OL]. [2024-03-20]. https://ww2.mathworks.cn/products/matlab.html.
[40] MULLINS G E, STANKIEWICZ P G, GUPTA S K. Automated generation of diverse and challenging scenarios for test and evaluation of autonomous vehicles[C]//Proceedings of the 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017: 1443-1450.
[41] KNIES C, DIERMEYER F. Data-driven test scenario generation for cooperative maneuver planning on highways[J]. Applied Sciences, 2020, 10(22): 8154.
[42] KESTING A, TREIBER M, HELBING D. General lane-changing model MOBIL for car-following models[J]. Transportation Research Record, 2007, 1999: 86-94.
[43] 邢星宇, 吴旭阳, 刘力豪, 等. 基于目标优化的自动驾驶决策规划系统自动化测试方法[J]. 同济大学学报 (自然科学版), 2021, 49(8): 1162-1169.
XING X Y, WU X Y, LIU L H, et al. Automatic testing method based on optimization algorithms for the decision and planning system of autonomous vehicles[J]. Journal of Tongji University (Natural Science), 2021, 49(8): 1162-1169.
[44] JIA L, YANG D, REN Y, et al. A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning[J]. Accident Analysis & Prevention, 2024, 194: 107279.
[45] TIAN Y, PEI K, JANA S, et al. DeepTest: automated testing of deep-neural-network-driven autonomous cars[C]//Proceedings of the 40th International Conference on Software Engineering, Gothenburg, Sweden, 2018: 303-314.
[46] Rambo. Steering angle model[EB/OL]. [2024-03-20]. https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/rambo.
[47] Chauffeur. Steering angle model[EB/OL].[2024-03-20].https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/chauffeur.
[48] Epoch. Steering angle model[EB/OL]. [2024-03-20]. https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/cg23.
[49] ZHANG M, ZHANG Y, ZHANG L, et al. DeepRoad: GAN-based metamorphic autonomous driving system testing[J]. arXiv:1802.02295, 2018.
[50] Autumn. Steering angle model[EB/OL].[2024-03-20]. https://github.com/udacity/self-driving-car/tree/master/steering-models/community-models/autumn.
[51] Rwightman. Steering angle model[EB/OL].[2024-03-20].https://github.com/udacity/self-driving-car/tree/master/steering-models/evaluation.
[52] ZHOU H, LI W, KONG Z, et al. DeepBillboard: systematic physical-world testing of autonomous driving systems[C]//Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, Seoul, South Korea, 2020: 347-358.
[53] BOJARSKI M, TESTA D D, DWORAKOWSKI D, et al. End to end learning for self-driving cars[J]. arXiv:1604.07316, 2016.
[54] DAVE_V2. A variation of Dave_V1[EB/OL]. [2024-03-20]. https://github.com/jacobgil/keras-steering-angle-visualizations.
[55] DAVE_V3. A variation of Dave_V1[EB/OL]. [2024-03-20]. https://github.com/navoshta/behavioral-cloning.
[56] LI Z, PAN M, ZHANG T, et al. Testing DNN-based autonomous driving systems under critical environmental conditions[C]//Proceedings of the 38th International Conference on Machine Learning, 2021: 6471-6482.
[57] Daveorig. End to end learning for self-driving cars[EB/OL]. [2024-03-20]. https://github. com/0bserver07/Nvidia-Autopilot-Keras.
[58] Dave-dropout. End to end learning for self-driving cars[EB/OL].[2024-03-20].https://github.com/alexstaravoitau/behavioral-cloning.
[59] STOCCO A, WEISS M, CALZANA M, et al. Misbehaviour prediction for autonomous driving systems[C]//Proceedings of the 42nd International Conference on Software Engineering, Seoul, South Korea, 2020: 359-371.
[60] DENG Y, LOU G, ZHENG J X, et al. BMT: behavior driven development-based metamorphic testing for autonomous driving models[C]//Proceedings of the 6th IEEE/ACM International Workshop on Metamorphic Testing, Madrid, Spain, 2021: 32-36.
[61] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA, 2015.
[62] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
[63] DING W, XU C, ARIEF M, et al. A survey on safety-critical driving scenario generation-a methodological perspective[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(7): 6971-6988.
[64] ZHOU X, SCHMEDDING A, REN H, et al. Strategic safety-critical attacks against an advanced driver assistance system[C]//Proceedings of the 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks, Baltimore, MD, USA, 2022: 79-87.
[65] BIRKEMEYER L, PETT T, VOGELSANG A, et al. Feature-interaction sampling for scenario-based testing of advanced driver assistance systems[C]//Proceedings of the 16th International Working Conference on Variability Modelling of Software-Intensive Systems, Florence, Italy, 2022: 1-10.
[66] ZOHDINASAB T, RICCIO V, GAMBI A, et al. Efficient and effective feature space exploration for testing deep learning systems[J]. ACM Transactions on Software Engineering and Methodology, 2023, 32(2): 1-38.
[67] 刘颖, 贺锦鹏, 刘卫国, 等. 自动紧急制动系统行人测试场景的研究[J]. 汽车技术, 2014(3): 35-39.
LIU Y, HE J P, LIU W G, et al. Research on test scenarios for AEB pedestrian system[J], Automobile Technology, 2014(3): 35-39.
[68] PreScan. Simcenter prescan software[EB/OL].[2024-03-20]. https://plm.sw.siemens.com/en-US/simcenter/autonomous-vehicle-solutions/prescan/.
[69] 张强, 黄俊富, 张胜根, 等. 基于自然驾驶数据的APS系统测试评价场景研究[C]//2018中国汽车工程学会年会. 北京: 中国汽车工程学会, 2018: 266-270.
ZHANG Q, HUANG J F, ZHANG S G, et al. Study of test and evaluation scenario for assisted parking system based on China-FOT[C]//2018 Annual Meeting of Chinese Society of Automotive Engineering. Beijing: China Society of Automotive Engineers, 2018: 266-270.
[70] GAMBI A, MüLLER M, FRASER G. AsFault: testing self-driving car software using search-based procedural content generation[C]//Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings, Montreal, QC, Canada, 2019: 27-30.
[71] LI A, CHEN S, SUN L, et al. SceGene: bio-inspired traffic scenario generation for autonomous driving testing[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 14859-14874.
[72] SUMO. Simulation of urban mobility[EB/OL]. [2024-03-20]. https://sumo.dlr.de/docs/index.html/.
[73] ABDESSALEM R B, PANICHELLA A, NEJATI S, et al. Testing autonomous cars for feature interaction failures using many-objective search[C]//Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, Montpellier, France, 2018: 143-154.
[74] ABDESSALEM R B, NEJATI S, BRIAND L C, et al. Testing vision-based control systems using learnable evolutionary algorithms[C]//Proceedings of the 40th International Conference on Software Engineering, Gothenburg, Sweden, 2018: 1016-1026.
[75] ABDESSALEM R B, PANICHELLA A, NEJATI S, et al. Automated repair of feature interaction failures in automated driving systems[C]//Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, 2020: 88-100.
[76] TIAN H, JIANG Y, WU G, et al. MOSAT: finding safety violations of autonomous driving systems using multi-objective genetic algorithm[C]//Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Singapore, 2022: 94-106.
[77] ZHANG C, LIU Y, ZHAO D, et al. RoadView: a traffic scene simulator for autonomous vehicle simulation testing[C]//Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China, 2014: 1160-1165.
[78] CARPIN S, LEWIS M, WANG J, et al. USARSim: a robot simulator for research and education[C]//Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Roma, Italy, 2007: 1400-1405.
[79] KOENIG N P, HOWARD A. Design and use paradigms for Gazebo, an open-source multi-robot simulator[C]//Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan, 2004: 2149-2154.
[80] NITSCHE P, THOMAS P, STUETZ R, et al. Pre-crash scenarios at road junctions: a clustering method for car crash data[J]. Accident Analysis & Prevention, 2017, 107: 137-151.
[81] FREMONT D J, KIM E, PANT Y V, et al. Formal scenario-based testing of autonomous vehicles: from simulation to the real world[C]//Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, Rhodes, Greece, 2020: 1-8.
[82] PONN T, BREITFUSS M, YU X, et al. Identification of challenging highway-scenarios for the safety validation of automated vehicles based on real driving data[C]//Proceedings of the Fifteenth International Conference on Ecological Vehicles and Renewable Energies, Monte-Carlo, Monaco, 2020: 1-10.
[83] KRAJEWSKI R, BOCK J, KLOEKER L, et al. The highD dataset: a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems[C]//Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, 2018: 2118-2125.
[84] WANG W, ZHAO D. Extracting traffic primitives directly from naturalistically logged data for self-driving applications[J]. arXiv:1709.03553, 2017.
[85] ZHANG W, WANG W. Learning V2V interactive driving patterns at signalized intersections[J]. Transportation Research Part C: Emerging Technologies, 2019, 108: 151-166.
[86] 陈吉清, 舒孝雄, 兰凤崇, 等. 典型危险事故特征的自动驾驶测试场景构建[J]. 华南理工大学学报 (自然科学版), 2021, 49(5): 1-8.
CHEN J Q, SHU X X, LAN F C, et al. Construction of automatic driving test scenarios with typical hazardous accident characteristics[J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(5): 1-8.
[87] GAMBI A, HUYNH T, FRASER G. Generating effective test cases for self-driving cars from police reports[C]//Proceedings of the ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Tallinn, Estonia, 2019: 257-267.
[88] CHEN C, SEFF A, KORNHAUSER A L, et al. DeepDriving: learning affordance for direct perception in autonomous driving[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), 2015: 2722-2730.
[89] ZHANG X, CAI Y. Building critical testing scenarios for autonomous driving from real accidents[C]//Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, Seattle, WA, USA, 2023: 462-474.
[90] 周文帅, 朱宇, 赵祥模, 等. 面向高速公路车辆切入场景的自动驾驶测试用例生成方法[J]. 汽车技术, 2021(1): 11-18.
ZHOU W S, ZHU Y, ZHAO X M, et al. Vehicle cut-in test case generation methods for testing of autonomous driving on highway[J]. Automobile Technology, 2021(1): 11-18.
[91] LEILABADI S H, SCHMIDT S. In-depth analysis of autonomous vehicle collisions in california[C]//Proceedings of the International Conference on Intelligent Transportation Systems, 2019: 889-893.
[92] WANG S, LI Z. Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches[J]. PLoS One, 2019, 14(3): e0214550.
[93] ZAMPETTI F, KAPUR R, PENTA M D, et al. An empirical characterization of software bugs in open-source cyber-physical systems[J]. Journal of Systems and Software, 2022, 192:111425.
[94] KLISCHAT M, LIU E I, HOLTKE F, et al. Scenario factory: creating safety-critical traffic scenarios for automated vehicles[C]//Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, Rhodes, Greece, 2020: 1-7.
[95] LI G, LI Y, JHA S, et al. AV-FUZZER: finding safety violations in autonomous driving systems[C]//Proceedings of the 31st IEEE International Symposium on Software Reliability Engineering, Coimbra, Portugal, 2020: 25-36.
[96] FENG S, FENG Y, YU C, et al. Testing scenario library generation for connected and automated vehicles, part I: methodology[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(3): 1573-1582.
[97] FENG S, FENG Y, SUN H, et al. Testing scenario library generation for connected and automated vehicles, part II: case studies[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(9): 5635-5647.
[98] RO J W, ROOP P S, MALIK A, et al. A formal approach for modeling and simulation of human car-following behavior[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(2): 639-648.
[99] KIM S, LIU M, RHEE J J, et al. DriveFuzz: discovering autonomous driving bugs through driving quality-guided fuzzing[C]//Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, Los Angeles, CA, USA, 2022: 1753-1767.
[100] KATO S, TOKUNAGA S, MARUYAMA Y, et al. Autoware on board: enabling autonomous vehicles with embedded systems[C]//Proceedings of the 9th ACM/IEEE International Conference on Cyber-Physical Systems, Porto, Portugal, 2018: 287-296.
[101] LUO Y, ZHANG X Y, ARCAINI P, et al. Targeting requirements violations of autonomous driving systems by dynamic evolutionary search[C]//Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, Melbourne, Australia, 2021: 279-291.
[102] CHENG M, ZHOU Y, XIE X. BehAVExplor: behavior diversity guided testing for autonomous driving systems[C]//Proceedings of the International Symposium on Software Testing and Analysis, 2023: 488-500.
[103] HUAI Y, ALMANEE S, CHEN Y, et al. scenoRITA: generating diverse, fully mutable, test scenarios for autonomous vehicle planning[J]. IEEE Transactions on Software Engineering, 2023, 49(10): 4656-4676.
[104] ZHANG X, ZHAO W, SUN Y, et al. Testing automated driving systems by breaking many laws efficiently[C]//Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, Seattle, WA, USA, 2023: 942-953.
[105] ZHANG Q, HONG D K, ZHANG Z, et al. A systematic framework to identify violations of scenario-dependent driving rules in autonomous vehicle software[C]//Proceedings of the ACM SIGMETRICS. International Conference on Measurement and Modeling of Computer Systems, 2021: 43-44.
[106] ZHOU Z Q, SUN L. Metamorphic testing of driverless cars[J]. Communications of the ACM, 2019, 62(3): 61-67.
[107] TUNCALI C E, FAINEKOS G. Rapidly-exploring random trees for testing automated vehicles[C]//Proceedings of the International Conference on Intelligent Transportation Systems, 2019: 661-666.
[108] ARCAINI P, ZHANG X Y, ISHIKAWA F. Less is more: simplification of test scenarios for autonomous driving system testing[C]//Proceedings of the 15th IEEE Conference on Software Testing, Verification and Validation, Valencia, Spain, 2022: 279-290.
[109] DONZé A. Breach, a toolbox for verification and parameter synthesis of hybrid systems[C]//Proceedings of the 22nd International Conference on Computer Aided Verification, Edinburgh, UK, 2010: 167-170.
[110] RIZALDI A, KEINHOLZ J, HUBER M, et al. Formalising and monitoring traffic rules for autonomous vehicles in Isabelle/HOL[C]//Proceedings of the 13th International Conference on Integrated Formal Methods, Turin, Italy, 2017: 50-66.
[111] BEGLEROVIC H, STOLZ M, HORN M. Testing of autonomous vehicles using surrogate models and stochastic optimization[C]//Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, Yokohama, Japan, 2017: 1-6.
[112] KLISCHAT M, ALTHOFF M. Generating critical test scenarios for automated vehicles with evolutionary algorithms[C]//Proceedings of the 2019 IEEE Intelligent Vehicles Symposium, Paris, France, 2019: 2352-2358.
[113] KLüCK F, ZIMMERMANN M, WOTAWA F, et al. Genetic algorithm-based test parameter optimization for ADAS system testing[C]//Proceedings of the 19th IEEE International Conference on Software Quality, Reliability and Security, Sofia, Bulgaria, 2019: 418-425.
[114] LEE R, KOCHENDERFER M, MENGSHOEL O, et al. Adaptive stress testing of airborne collision avoidance systems[C]//Proceedings of the 2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC), 2015: 1-24.
[115] KOREN M, ALSAIF S, LEE R, et al. Adaptive stress testing for autonomous vehicles[C]//Proceedings of the 2018 IEEE Intelligent Vehicles Symposium, Changshu, China, 2018: 1-7.
[116] JENKINS I R, GEE L O, KNAUSS A, et al. Accident scenario generation with recurrent neural networks[C]//Proceedings of the International Conference on Intelligent Transportation Systems, 2018: 3340-3345.
[117] DEMETRIOU A, ALFSV?G H, RAHROVANI S, et al. A deep learning framework for generation and analysis of driving scenario trajectories[J]. SN Computer Science, 2023, 4(3): 251.
[118] PRIISALU M, PADURARU C, SMINCHISESCU C. Varied realistic autonomous vehicle collision scenario generation[C]//Proceedings of the 22nd Scandinavian Conference on Image Analysis, 2023: 354-372.
[119] ZHONG Z, KAISER G E, RAY B. Neural network guided evolutionary fuzzing for finding traffic violations of autonomous vehicles[J]. IEEE Transactions on Software Engineering, 2023, 49(4): 1860-1875.
[120] DENG Y, YAO J, TU Z, et al. TARGET: traffic rule-based test generation for autonomous driving systems[J]. arXiv: 2305.06018, 2023.
[121] ZHANG Q, TANG M, GENG R, et al. MMFN: multi-modal-fusion-net for end-to-end driving[C]//Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyoto, Japan, 2022: 8638-8643.
[122] CHEN D, KR?HENBüHL P. Learning from all vehicles[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 2022: 17201-17210.
[123] 赵祥模国家重点研发计划 (2021YFB2501200) 团队. 自动驾驶测试与评价技术研究进展[J]. 交通运输工程学报, 2023, 23(6): 10-77.
ZHAO Xiangmo’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.
[124] CarMaker. Vehicle dynamics simulation software[EB/OL]. [2024-03-20]. https://ipg-automotive. com/cn/products-solutions/software/carmaker/.
[125] 秦严严. 交通流分析理论[M]. 北京: 人民交通出版社. 2023.
QIN Y Y. Theory of traffic flow analysis[M]. Beijing: China Communications Press, 2023.
[126] YU H, JIANG R, HE Z, et al. Automated vehicle-involved traffic flow studies: a survey of assumptions, models, speculations, and perspectives[J]. Transportation Research Part C: Emerging Technologies, 2021, 127: 103101.
[127] Vissim. Traffic simulation software[EB/OL].[2024-03-20]. https://www. ptvgroup. com/zh-hant/products/ptv-vissim/.
[128] VTD. Complete tool-chain for driving simulation applications[EB/OL].[2024-03-20].https://hexagon.com/products/virtual-test-drive/.
[129] Panosim. Integrated simulation test system for automobile automatic driving[EB/OL].[2024-03-20].https://www. panosim.com/.
[130] CarCraft. Autonomous driving simulation test software[EB/OL].[2024-03-20]. https://www. waymo. com/.
[131] AirSim. A simulation platform for AI research and experimentation[EB/OL].[2024-03-20].https://microsoft. github. io/AirSim/.
[132] ZHANG X, TAO J, TAN K, et al. Finding critical scenarios for automated driving systems: a systematic mapping study[J]. IEEE Transactions on Software Engineering, 2023, 49(3): 991-1026.
[133] STOCCO A, PULFER B, TONELLA P. Mind the gap! A study on the transferability of virtual vs physical-world testing of autonomous driving systems[J]. arXiv:2112. 11255, 2021.
[134] CHEN S T, CHEN Y, ZHANG S, et al. A novel integrated simulation and testing platform for self-driving cars with hardware in the loop[J]. IEEE Transactions on Intelligent Vehicles, 2019, 4(3): 425-436.