Page 106 - 《软件学报》2025年第8期

杜德慧 等: 面向自动驾驶系统的场景建模及边缘关键场景生成 3529

Vision. Tel Aviv: Springer, 2022. 668–683. [doi: 10.1007/978-3-031-19824-3_39]
[12] Müller T, McWilliams B, Rousselle F, Gross M, Novák J. Neural importance sampling. ACM Trans. on Graphics (TOG), 2019, 38(5):
145. [doi: 10.1145/3341156]
[13] Zhao D, Lam H, Peng H, Bao S, Leblanc DJ, Nobukawa K, Pan CS. Accelerated evaluation of automated vehicles safety in lane-change
scenarios based on importance sampling techniques. IEEE Trans. on Intelligent Transportation Systems, 2017, 18(3): 595–607. [doi: 10.
1109/TITS.2016.2582208]
[14] Feng S, Yan XT, Sun HW, Feng YH, Liu HX. Intelligent driving intelligence test for autonomous vehicles with naturalistic and
adversarial environment. Nature Communications, 2021, 12(1): 748. [doi: 10.1038/s41467-021-21007-8]
[15] Arief M, Huang ZY, Kumar GKS, Bai YL, He SY, Ding WH, Lam H, Zhao D. Deep probabilistic accelerated evaluation: A robust
certifiable rare-event simulation methodology for black-box safety-critical systems. In: Proc. of the 24th Int’l Conf. on Artificial
Intelligence and Statistics. PMLR, 2021. 595–603.
[16] Li B, Du DH, Chen SC, Wei MJ, Zheng CH, Zhang XY. SML4ADS: An open DSML for autonomous driving scenario representation and
generation. In: Proc. of the 37th IEEE/ACM Int’l Conf. on Automated Software Engineering. Rochester: ACM, 2022. 145. [doi: 10.1145/
3551349.3561169]
[17] Du DH, Li B, Zheng CH, Zhang XY. A novel and pragmatic scenario modeling framework with verification-in-the-loop for autonomous
driving systems. In: Proc. of the 45th IEEE/ACM Int’l Conf. on Software Engineering: New Ideas and Emerging Results. Melbourne:
IEEE, 2023. 84–89. [doi: 10.1109/ICSE-NIER58687.2023.00021]
[18] Li B. Research on modeling, simulation and verification of autonomous driving scenarios [MS. Thesis]. Shanghai: East China Normal
University, 2022 (in Chinese with English abstract). [doi: 10.27149/d.cnki.ghdsu.2022.005494]
[19] Deng WW, LI JK, Ren BT, Wang WQ, Ding J. A survey on automatic simulation scenario generation methods for autonomous driving.
China Journal of Highway and Transport, 2022, 35(1): 316–333 (in Chinese with English abstract). [doi: 10.3969/j.issn.1001-7372.2022.
01.027]
[20] Nilsson J, Sjöberg J. Strategic decision making for automated driving on two-lane, one way roads using model predictive control. In:
Proc. of the 2013 IEEE Intelligent Vehicles Symp. Gold Coast: IEEE, 2013. 1253–1258. [doi: 10.1109/IVS.2013.6629638]
[21] ASAM. OpenDRIVE Standard. 2021. https://www.asam.net/standards/detail/opendrive/
[22] Barsi A, Poto V, Tihanyi V. Creating OpenCRG road surface model from terrestrial laser scanning data for autonomous vehicles. In:
Proc. of the 2nd Vehicle and Automotive Engineering Conf. Miskolc: Springer, 2018. 361–369. [doi: 10.1007/978-3-319-75677-6_30]
[23] Chaplier J, That TN, Hewatt M, Gallée G. Toward a standard: RoadXML, the road network database format. In: Proc. of the 2010 Driving
Simulation Conf. 2010. 211–220.
[24] Haklay M, Weber P. OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 2008, 7(4): 12–18. [doi: 10.1109/MPRV.
2008.80]
[25] Althoff M, Koschi M, Manzinger S. CommonRoad: Composable benchmarks for motion planning on roads. In: Proc. of the 2017 IEEE
Intelligent Vehicles Symp. Los Angeles: IEEE, 2017. 719–726. [doi: 10.1109/IVS.2017.7995802]
[26] ASAM. OpenSCENARIO Standard. 2021. https://www.asam.net/standards/detail/openscenario/v200/
[27] Fremont DJ, Dreossi T, Ghosh S, Yue XY, Sangiovanni-Vincentelli AL, Seshia SA. Scenic: A language for scenario specification and
scene generation. In: Proc. of the 40th ACM SIGPLAN Conf. on Programming Language Design and Implementation. Phoenix: ACM,
2019. 63–78. [doi: 10.1145/3314221.3314633]
[28] Dosovitskiy A, Ros G, Codevilla F, Lopez A, Koltun V. CARLA: An open urban driving simulator. In: Proc. of the 1st Annual Conf. on
Robot Learning. Mountain View: PMLR, 2017. 1–16.
[29] Menzel T, Bagschik G, Maurer M. Scenarios for development, test and validation of automated vehicles. In: Proc. of the 2018 IEEE
Intelligent Vehicles Symp. Changshu: IEEE, 2018. 1821–1827. [doi: 10.1109/IVS.2018.8500406]
[30] Chen W, Kloul L. An ontology-based approach to generate the advanced driver assistance use cases of highway traffic. In: Proc. of the
10th Int’l Joint Conf. on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS, 2018. 75–83.
[31] Elgharbawy M, Schwarzhaupt A, Frey M, Gauterin F. Ontology-based adaptive testing for automated driving functions using data mining
techniques. Transportation Research Part F: Traffic Psychology and Behaviour, 2019, 66: 234–251. [doi: 10.1016/j.trf.2019.07.021]
[32] Hungar H. A concept of scenario space exploration with criticality coverage guarantees. In: Proc. of the 9th Int’l Symp. on Leveraging
Applications of Formal Methods. Rhodes: Springer, 2020. 293–306. [doi: 10.1007/978-3-030-61467-6_19]
[33] Klischat M, Althoff M. Generating critical test scenarios for automated vehicles with evolutionary algorithms. In: Proc. of the 2019 IEEE
Intelligent Vehicles Symp. Paris: IEEE, 2019. 2352–2358. [doi: 10.1109/IVS.2019.8814230]
[34] Tuncali CE, Pavlic TP, Fainekos G. Utilizing S-TaLiRo as an automatic test generation framework for autonomous vehicles. In: Proc. of