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朱向雷 等:自动驾驶智能系统测试研究综述 2075
[35] Wang Z, Yan M, Liu S, Chen JJ, Zhang DD, Wu Z, Chen X. Survey on testing of deep neural networks. Ruan Jian Xue Bao/Journal
of Software, 2020,31(5):1255−1275 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/5951.htm [doi: 10.13328/
j.cnki.jos.005951]
[36] Pei K, Cao Y, Yang J, et al. Deepxplore: Automated whitebox testing of deep learning systems. In: Proc. of the 26th Symp. on
Operating Systems Principles. 2017. 1–18.
[37] Laurent T, Arcaini P, Ishikawa F, et al. A mutation-based approach for assessing weight coverage of a path planner. In: Proc. of the
26th Asia-Pacific Software Engineering Conf. (APSEC). IEEE, 2019. 94–101.
[38] Bühler O, Wegener J. Automatic testing of an autonomous parking system using evolutionary computation. SAE Technical Report,
2004.
[39] Bühler O, Wegener J. Evolutionary functional testing. Computers & Operations Research, 2008,35(10):3144–3160.
[40] Abdessalem RB, Nejati S, Briand LC, et al. Testing vision-based control systems using learnable evolutionary algorithms. In: Proc.
of the 40th IEEE/ACM Int’l Conf. on Software Engineering (ICSE). IEEE, 2018. 1016–1026.
[41] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. on Evolutionary
Computation, 2002,6(2):182–197.
[42] Beyer HG, Deb K. On self-adaptive features in real-parameter evolutionary algorithms. IEEE Trans. on Evolutionary Computation,
2001,5(3):250–270.
[43] Deb K, Agrawal RB. Simulated binary crossover for continuous search space. Complex Systems, 1995,9(2):115–148.
[44] Knowles JD, Thiele L, Zitzler E. A tutorial on the performance assessment of stochastic multiobjective optimizers. TIK-Report,
2006. 214.
[45] Abdessalem RB, Nejati S, Briand LC, et al. Testing advanced driver assistance systems using multi-objective search and neural
networks. In: Proc. of the 31st IEEE/ACM Int’l Conf. on Automated Software Engineering. 2016. 63–74.
[46] Abdessalem RB, Panichella A, Nejati S, et al. Testing autonomous cars for feature interaction failures using many-objective search.
In: Proc. of the 33rd IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). IEEE, 2018. 143–154.
[47] Panichella A, Kifetew FM, Tonella P. Reformulating branch coverage as a many-objective optimization problem. In: Proc. of the
8th IEEE Int’l Conf. on Software Testing, Verification and Validation (ICST). IEEE, 2015. 1–10.
[48] Gietelink O, Ploeg J, De Schutter B, et al. Development of advanced driver assistance systems with vehicle hardware-in-the-loop
simulations. Vehicle System Dynamics, 2006,44(7):569–590.
[49] Belbachir A, Smal JC, Blosseville JM, et al. Simulation-driven validation of advanced driving-assistance systems. Procedia-social
and Behavioral Sciences, 2012,48:1205–1214.
[50] Gruyer D, Glaser S, Pechberti S, et al. Distributed simulation architecture for the design of cooperative ADAS. In: Proc. of the 1st
Int’l Symp. on Future Active Safety Technology Toward Zero-traffic-accident. 2011.
[51] Hiblot N, Gruyer D, Barreiro JS, et al. Pro-sivic and roads—a software suite for sensors simulation and virtual prototyping of
ADAS. In: Proc. of the DSC. 2010. 277–288.
[52] Abdessalem Ben R, Panichella A, Nejati S, et al. Automated repair of feature interaction failures in automated driving systems. In:
Proc. of the 29th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis (ISSTA 2020). 2020.
[53] Jones JA, Harrold MJ, Stasko J. Visualization of test information to assist fault localization. In: Proc. of the 24th Int’l Conf. on
Software Engineering (ICSE 2002). IEEE, 2002. 467–477.
[54] Gambi A, Mueller M, Fraser G. Automatically testing self-driving cars with search-based procedural content generation. In: Proc. of
the 28th ACM SIGSOFT Int’l Symp. on Software Testing and Analysis. 2019. 318–328.
[55] China Automotive Technology and Research Center Co., Ltd. Development and Application of Autonomous Driving Test Scenario
Technology. Beijing: China Machine Press, 2020 (in Chinese).
[56] Ulbrich S, Menzel T, Reschka A, et al. Defining and substantiating the terms scene, situation, and scenario for automated driving.
In: Proc. of the 18th IEEE Int’l Conf. on Intelligent Transportation Systems. IEEE, 2015. 982–988.
[57] Zhang C, Liu Y, Zhao D, et al. RoadView: A traffic scene simulator for autonomous vehicle simulation testing. In: Proc. of the 17th
Int’l IEEE Conf. on Intelligent Transportation Systems (ITSC). IEEE, 2014. 1160–1165.
[58] Althoff M, Lutz S. Automatic generation of safety-critical test scenarios for collision avoidance of road vehicles. In: Proc. of the
2018 IEEE Intelligent Vehicles Symp. (IV). IEEE, 2018. 1326–1333.
[59] Althoff M, Koschi M, Manzinger S. CommonRoad: Composable benchmarks for motion planning on roads. In: Proc. of the 2017
IEEE Intelligent Vehicles Symp. (IV). IEEE, 2017. 719–726.