[8]
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A., 2017. Improved training of Wasserstein GANs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, 5769–5779.
[9]
He, Y., Liu, Y., Yang, L., Qu, X., 2023. Deep adaptive control: Deep reinforcement learning-based adaptive vehicle trajectory control algorithms for different risk levels. IEEE Trans Intell Veh. Http://doi.org/10.1109/TIV.2023.3303408.
[11]
Hoseini, F., Rahrovani, S., Chehreghani, M. H., 2021. Vehicle motion trajectories clustering via embedding transitive relations. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 1314–1321.
[12]
Hu, X., Zhu, B., Tan, D., Zhang, N., Wang, Z., 2022. Test scenario generation method for autonomous vehicles based on combinatorial testing and Bayesian network. Proc Inst Mech Eng Part D J Automob Eng, 095440702211255.
[13]
Jenkins, I. R., Gee, L. O., Knauss, A., Yin, H., Schroeder, J., 2018. Accident scenario generation with recurrent neural networks. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 3340–3345.
[14]
Krajewski, R., Moers, T., Nerger, D., Eckstein, L., 2018a. Data-driven maneuver modeling using generative adversarial networks and variational autoencoders for safety validation of highly automated vehicles. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2383–2390.
[15]
Krajewski, R., Bock, J., Kloeker, L., Eckstein, L., 2018b. The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2118–2125.
[16]
Li, P., Jin, S., Hu, W., Gao, L., Che, Y., Tan, Z., Dong, X., 2022. Complexity evaluation of vehicle-vehicle accident scenarios for autonomous driving simulation tests. J Automo Saf Energy, 13, 697–704. (in Chinese)
[21]
Liu, Y., Wu, F., Liu, Z., Wang, K., Wang, F., Qu, X., 2023. Can language models be used for real-world urban-delivery route optimization? Innovation, 4, 100520.
[23]
Pei, H., Ren, K., Yang, Y., Liu, C., Qin, T., Li, D., 2021. Towards generating real-world time series data. In: 2021 IEEE International Conference on Data Mining (ICDM), 469–478.
[24]
Rocklage, E., Kraft, H., Karatas, A., Seewig, J., 2017. Automated scenario generation for regression testing of autonomous vehicles. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 476–483.
[25]
Shu, H., Yuan, K., Xiu, H. L., Xia, Q., He, S., 2019. Construction of basic test scenarios of automated vehicles. China J Highw Transp, 32, 245–254. (in Chinese)
[26]
Tan, S., Wong, K., Wang, S., Manivasagam, S., Ren, M., Urtasun, R., 2021. SceneGen: learning to generate realistic traffic scenes. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 892–901.
[27]
Ulbrich, S., Menzel, T., Reschka, A., Schuldt, F., Maurer, M., 2015. Defining and substantiating the terms scene, situation, and scenario for automated driving. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), 982–988.
[28]
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al., 2017. Attention is all You need. https://arxiv.org/abs/1706.03762.pdf
[30]
Yang, Y., Wang, Y., Yin, C., Ji, Q., 2021. Simulation testing scenario generation for comfort evaluation of automated vehicles. In: 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI), 1–6.
[31]
Yoon, J., Jarrett, D., Van der Schaar, M., 2019. Time-series generative adversarial networks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems, 5508–5518.
[32]
Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T., 2016. UnitBox: An advanced object detection network. In: Proceedings of the 24th ACM international conference on Multimedia, 516–520.
[33]
Zhu, Y., Zhao, X. M., Xu, Z. G., Wang, R. M., 2022. Automatic generation algorithm of lane-change virtual test scenario on highways for automated vehicles using Monte Carlo simulation. China J Highw Transp, 35, 89–100. (in Chinese)