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2074 Journal of Software 软件学报 Vol.32, No.7, July 2021

[10] Lu J, Sibai H, Fabry E, et al. No need to worry about adversarial examples in object detection in autonomous vehicles. arXiv
Preprint arXiv: 1707.03501, 2017.
[11] Daftry S, Zeng S, Bagnell JA, et al. Introspective perception: Learning to predict failures in vision systems. In: Proc. of the 2016
IEEE/RSJ Int’l Conf. on Intelligent Robots and Systems (IROS). IEEE, 2016. 1743–1750.
[12] Gurău C, Rao D, Tong CH, et al. Learn from experience: Probabilistic prediction of perception performance to avoid failure. The
Int’l Journal of Robotics Research, 2018,37(9):981–995.
[13] Ramanagopal MS, Anderson C, Vasudevan R, et al. Failing to learn: Autonomously identifying perception failures for self-driving
cars. IEEE Robotics and Automation Letters, 2018,3(4):3860–3867.
[14] Johnson-Roberson M, Barto C, Mehta R, et al. Driving in the matrix: Can virtual worlds replace human-generated annotations for
real world tasks? arXiv Preprint arXiv: 1610.01983, 2016.
[15] Dreossi T, Ghosh S, Sangiovanni-Vincentelli A, et al. Systematic testing of convolutional neural networks for autonomous driving.
arXiv Preprint arXiv: 1708.03309, 2017.
[16] Papernot N, McDaniel P, Jha S, et al. The limitations of deep learning in adversarial settings. In: Proc. of the 2016 IEEE European
Symp. on Security and Privacy (EuroS&P). IEEE, 2016. 372–387.
[17] Talwar D, Guruswamy S, Ravipati N, et al. Evaluating validity of synthetic data in perception tasks for autonomous vehicles. In:
Proc. of the 2020 IEEE Int’l Conf. on Artificial Intelligence Testing (AITest). IEEE, 2020. 73–80.
[18] LGSVL Simulator. 2019. https://github.com/lgsvl/simulator-2019.05-obsolete
[19] Araiza-Illan D, Pipe AG, Eder K. Model-based test generation for robotic software: Automata versus belief-desire-intention agents.
arXiv Preprint arXiv: 1609.08439, 2016.
[20] Bordini RH, Hübner JF, Wooldridge M. Programming Multi-agent Systems in AgentSpeak using Jason. John Wiley & Sons, 2007.
[21] Cordts M, Omran M, Ramos S, et al. The cityscapes dataset for semantic urban scene understanding. In: Proc. of the IEEE Conf. on
Computer Vision and Pattern Recognition. 2016. 3213–3223.
[22] China Automotive Technology and Research Center Co., Ltd. Intelligent Connected Vehicle Technology. 2nd ed., Beijing: Social
Science Academic Press, 2020 (in Chinese).
[23] Schultz AC, Grefenstette JJ, De Jong KA. Adaptive testing of controllers for autonomous vehicles. In: Proc. of the 1992 Symp. on
Autonomous Underwater Vehicle Technology. IEEE, 1992. 158–164.
[24] Tuncali CE, Pavlic TP, Fainekos G. Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles. In:
Proc. of the 19th IEEE Int’l Conf. on Intelligent Transportation Systems (ITSC). IEEE, 2016. 1470–1475.
[25] Mullins GE, Stankiewicz PG, Gupta SK. Automated generation of diverse and challenging scenarios for test and evaluation of
autonomous vehicles. In: Proc. of the 2017 IEEE Int’l Conf. on Robotics and Automation (ICRA). IEEE, 2017. 1443–1450.
[26] Koren M, Alsaif S, Lee R, et al. Adaptive stress testing for autonomous vehicles. In: Proc. of the 2018 IEEE Intelligent Vehicles
Symposium (IV). IEEE, 2018. 1–7.
[27] Zhou H, Li W, Zhu Y, et al. Deepbillboard: Systematic physical-world testing of autonomous driving systems. arXiv Preprint arXiv:
1812.10812, 2018.
[28] Tian Y, Pei K, Jana S, et al. Deeptest: Automated testing of deep-neural-network-driven autonomous cars. In: Proc. of the 40th Int’l
Conf. on Software Engineering. 2018. 303–314.
[29] Zhang M, Zhang Y, Zhang L, et al. DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous
driving systems. In: Proc. of the 33rd IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). IEEE, 2018. 132–142.
[30] Stocco A, Weiss M, Calzana M, et al. Misbehaviour prediction for autonomous driving systems. arXiv Preprint arXiv: 1910.04443,
2019.
[31] D’Ambrosio J, Adiththan A, Ordoukhanian E, et al. An MBSE approach for development of resilient automated automotive systems.
Systems, 2019,7(1):1.
[32] Crombecq K, De Tommasi L, Gorissen D, et al. A novel sequential design strategy for global surrogate modeling. In: Proc. of the
Winter Simulation Conf. 2009. 731–742.
[33] Treiber M, Hennecke A, Helbing D. Congested traffic states in empirical observations and microscopic simulations. Physical
Review E, 2000,62(2):1805.
[34] Alhaija HA, Mustikovela SK, Mescheder L, et al. Augmented reality meets computer vision: Efficient data generation for urban
driving scenes. Int’l Journal of Computer Vision, 2018,126(9):961–972.