DAnoScenE: a driving anomaly scenario extraction framework for autonomous vehicles in urban streets

References

• Alambeigi, H., McDonald, A. D., & Tankasala, S. R. (2020). Crash themes in automated vehicles: A topic modeling analysis of the California Department of Motor Vehicles automated vehicle crash database. arXiv preprint arXiv:2001.11087. https://doi.org/10.48550/arXiv.2001.11087
   Google Scholar
• Alexiadis, V., Colyar, J., Halkias, J., Hranac, R., & McHale, G. (2004). The next generation simulation program. Institute of Transportation Engineers. ITE Journal, 74(8), 22–26.
   Web of Science ®Google Scholar
• Anguelov, D. (2019). Taming the long tail of autonomous driving challenges. https://www.youtube.com/watch?v=Q0nGo2-y0xY
   Google Scholar
• Ashraf, M. T., Dey, K., Mishra, S., & Rahman, M. T. (2021). Extracting rules from autonomous-vehicle-involved crashes by applying decision tree and association rule methods. Transportation Research Record: Journal of the Transportation Research Board, 2675(11), 522–533. https://doi.org/10.1177/03611981211018461
   Web of Science ®Google Scholar
• Batsch, F., Kanarachos, S., Cheah, M., Ponticelli, R., & Blundell, M. (2021). A taxonomy of validation strategies to ensure the safe operation of highly automated vehicles. Journal of Intelligent Transportation Systems, 26(1), 14–33. https://doi.org/10.1080/15472450.2020.1738231
   Web of Science ®Google Scholar
• Beinstein, A., & Sumers, T. (2016). How Uber engineering increases safe driving with telematics. Uber Engineering Blog.
   Google Scholar
• Biever, W., Angell, L., & Seaman, S. (2020). Automated driving system collisions: Early lessons. Human Factors, 62(2), 249–259. https://doi.org/10.1177/0018720819872034
   PubMed Web of Science ®Google Scholar
• Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020). nuScenes: A multimodal dataset for autonomous driving [Paper presentation]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11621–11631). https://doi.org/10.1109/CVPR42600.2020.01164
   Google Scholar
• Caesar, H., Kabzan, J., Tan, K. S., Fong, W. K., Wolff, E., Lang, A., Fletcher, L., Beijbom, S., & Omari, S. (2021). nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles. arXiv preprint arXiv:2106.11810. https://doi.org/10.48550/arXiv.2106.11810
   Google Scholar
• Chang, M. F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., Hays, J. (2019). Argoverse: 3d tracking and forecasting with rich maps. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8748–-8757.) https://doi.org/10.1109/cvpr.2019.00895
   Google Scholar
• Da, F. (2022). Comprehensive reactive safety : No need for a trajectory if you have a strategy [Paper presentation]. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS ) (pp. 2903–2910). IEEE. https://doi.org/10.1109/IROS47612.2022.9981757
   Google Scholar
• Ettinger, S., Cheng, S., Caine, B., Liu, C., Zhao, H., Pradhan, S., Chai, Y., Sapp, B., Qi, C. R., Zhou, Y., Yang, Z., Chouard, A., Sun, P., Ngiam, J., Vasudevan, V., McCauley, A., Shlens, J., Anguelov, D. (2021). Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset . In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9710–9719.). https://doi.org/10.1109/iccv48922.2021.00957
   Google Scholar
• Fard, M. R., Mohaymany, A. S., & Shahri, M. (2017). A new methodology for vehicle trajectory reconstruction based on wavelet analysis. Transportation Research Part C: Emerging Technologies, 74, 150–167. https://doi.org/10.1016/j.trc.2016.11.010
   Web of Science ®Google Scholar
• Feng, S., Sun, H., Yan, X., Zhu, H., Zou, Z., Shen, S., & Liu, H. X. (2023). Dense reinforcement learning for safety validation of autonomous vehicles. Nature, 615(7953), 620–627. https://doi.org/10.1038/s41586-023-05732-2
   PubMed Web of Science ®Google Scholar
• Houston, J., Zuidhof, G., Bergamini, L., Ye, Y., Chen, L., Jain, A., Omari, S., Iglovikov, V., & Ondruska, P. (2021). One thousand and one hours: Self-driving motion prediction dataset [Paper presentation]. In Conference on Robot Learning (pp. 409–418). PMLR.
   Google Scholar
• Hu, X., Zheng, Z., Chen, D., Zhang, X., & Sun, J. (2022). Processing, assessing, and enhancing the Waymo autonomous vehicle open dataset for driving behavior research. Transportation Research Part C: Emerging Technologies, 134, 103490. https://doi.org/10.1016/j.trc.2021.103490
   Web of Science ®Google Scholar
• Hu, Y., & Zhao, D. (2023). Extraction and analysis of autonomous vehicle crash scenarios based on crash sequences. Science Technology & Engineering, 23(11), 4908–4916. https://doi.org/10.12404/j.issn.1671-1815.2023.23.11.04908
   Google Scholar
• Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., & Yang, R. (2018). The ApolloScape dataset for autonomous driving [Paper presentation]. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 954–960). https://doi.org/10.1109/CVPRW.2018.00141
   Google Scholar
• Jain, A., Del Pero, L., Grimmett, H., & Ondruska, P. (2021). Autonomy 2.0: Why is self-driving always 5 years away?. arXiv preprint arXiv:2107.08142. https://doi.org/10.48550/arXiv.2107.08142
   Google Scholar
• Kalra, N., & Paddock, S. M. (2016). Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability? Transportation Research Part A: Policy and Practice, 94, 182–193. https://doi.org/10.1016/j.tra.2016.09.010
   Web of Science ®Google Scholar
• Kang, M., Song, J., Hwang, K. (2022). The extraction of automated vehicles traffic accident factors and scenarios using real-world data . In Congress on Intelligent Systems: Proceedings of CIS 2021 (Vol. 1, pp. 1–15.). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-9416-5\_1
   Google Scholar
• Klischat, M., Liu, E. I., Holtke, F., & Althoff, M. (2020). Scenario factory: Creating safety-critical traffic scenarios for automated vehicles [Paper presentation]. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) (pp. 1–7). IEEE. https://doi.org/10.1109/ITSC45102.2020.9294629
   Google Scholar
• Krajewski, R., Bock, J., Kloeker, L., & Eckstein, L. (2018). The highD dataset: A drone dataset of naturalistic vehicle trajectories on German highways for validation of highly automated driving systems [Paper presentation]. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 2118–2125). IEEE. https://doi.org/10.1109/ITSC.2018.8569552
   Google Scholar
• Laureshyn, A., & Várhelyi, A. (2018). The Swedish traffic conflict technique: Observer’s manual. Lund University.
   Google Scholar
• Li, Q., Peng, Z., Feng, L., Duan, C., Mo, W., & Zhou, B. (2023). ScenarioNet: Open-source platform for large-scale traffic scenario simulation and modeling. arXiv preprint arXiv:2306.12241. https://doi.org/10.48550/arXiv.2306.12241
   Google Scholar
• Li, Q., Peng, Z., Feng, L., Zhang, Q., Xue, Z., & Zhou, B. (2022). MetaDrive: Composing diverse driving scenarios for generalizable reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), 3461–3475. https://doi.org/10.1109/TPAMI.2022.3190471
   Web of Science ®Google Scholar
• Liu, J., & Khattak, A. J. (2016). Delivering improved alerts, warnings, and control assistance using basic safety messages transmitted between connected vehicles. Transportation Research Part C: Emerging Technologies, 68, 83–100. https://doi.org/10.1016/j.trc.2016.03.009
   Web of Science ®Google Scholar
• Liu, J., & Khattak, A. J. (2020). Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles. Journal of Intelligent Transportation Systems, 24(1), 11–23. https://doi.org/10.1080/15472450.2019.1699076
   Web of Science ®Google Scholar
• Liu, Q., Wang, X., Wu, X., Glaser, Y., & He, L. (2021). Crash comparison of autonomous and conventional vehicles using pre-crash scenario typology. Accident; Analysis and Prevention, 159, 106281. https://doi.org/10.1016/j.aap.2021.106281
   PubMed Web of Science ®Google Scholar
• McKinsey. (2013). Disruptive technologies: Advances that will transform life, business, and the global economy. McKinsey Global Institute.
   Google Scholar
• Montanino, M., & Punzo, V. (2015). Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns. Transportation Research Part B: Methodological, 80, 82–106. https://doi.org/10.1016/j.trb.2015.06.010
   Web of Science ®Google Scholar
• Mueller, A. S., Cicchino, J. B., & Zuby, D. S. (2020). What humanlike errors do autonomous vehicles need to avoid to maximize safety? Journal of Safety Research, 75, 310–318. https://doi.org/10.1016/j.jsr.2020.10.005
   PubMed Web of Science ®Google Scholar
• Nayakanti, N., Al-Rfou, R., Zhou, A., Goel, K., Refaat, K. S., & Sapp, B. (2023). Wayformer: Motion forecasting via simple & efficient attention networks [Paper presentation]. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2980–2987). IEEE. https://doi.org/10.1109/ICRA48891.2023.10160609
   Google Scholar
• Petrović, Ð., Mijailović, R., & Pešić, D. (2020). Traffic accidents with autonomous vehicles: Type of collisions, manoeuvres and errors of conventional vehicles’ drivers. Transportation Research Procedia, 45, 161–168. https://doi.org/10.1016/j.trpro.2020.03.003
   Google Scholar
• Punzo, V., Borzacchiello, M. T., & Ciuffo, B. (2011). On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data. Transportation Research Part C: Emerging Technologies, 19(6), 1243–1262. https://doi.org/10.1016/j.trc.2010.12.007
   Web of Science ®Google Scholar
• Qi, C. R., Su, H., Mo, K., Guibas, L. J. (2017). Pointnet: Deep learning on point sets for 3d classification and segmentation . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 652–660.). https://doi.org/10.1109/cvpr.2017.16
   Google Scholar
• Qi, C. R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., Anguelov, D. (2021). Offboard 3d object detection from point cloud sequences . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6134–6144.). https://doi.org/10.1109/cvpr46437.2021.00607
   Google Scholar
• Qiu, Y., Misu, T., & Busso, C. (2023). Unsupervised scalable multimodal driving anomaly detection. IEEE Transactions on Intelligent Vehicles, 8(4), 3154–3165. https://doi.org/10.1109/TIV.2022.3160861
   Google Scholar
• Scanlon, J. M., Kusano, K. D., Daniel, T., Alderson, C., Ogle, A., & Victor, T. (2021). Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain. Accident; Analysis and Prevention, 163, 106454. https://doi.org/10.1016/j.aap.2021.106454
   PubMed Web of Science ®Google Scholar
• Shah, T., Lepird, J. R., Hartnett, A. T., & Dolan, J. M. (2021, September). A simulation-based benchmark for behavioral anomaly detection in autonomous vehicles [Paper presentation]. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) (pp. 2074–2081). IEEE. https://doi.org/10.1109/ITSC48978.2021.9565042
   Google Scholar
• Shi, S., Jiang, L., Dai, D., & Schiele, B. (2022). Motion transformer with global intention localization and local movement refinement. Advances in Neural Information Processing Systems, 35, 6531–6543.
   Google Scholar
• So, J., Hwangbo, J., Kim, S. H., & Yun, I. (2023). Analysis on autonomous vehicle detection performance according to various road geometry settings. Journal of Intelligent Transportation Systems, 27(3), 384–395. https://doi.org/10.1080/15472450.2022.2042280
   Web of Science ®Google Scholar
• Song, Y., Chitturi, M. V., & Noyce, D. A. (2021). Automated vehicle crash sequences: Patterns and potential uses in safety testing. Accident; Analysis and Prevention, 153, 106017. https://doi.org/10.1016/j.aap.2021.106017
   PubMed Web of Science ®Google Scholar
• Utriainen, R. (2020). The potential impacts of automated vehicles on pedestrian safety in a four-season country. Journal of Intelligent Transportation Systems, 25(2), 188–196. https://doi.org/10.1080/15472450.2020.1845671
   Web of Science ®Google Scholar
• Wiederer, J., Schmidt, J., Kressel, U., Dietmayer, K., & Belagiannis, V. (2022). A benchmark for unsupervised anomaly detection in multi-agent trajectories [Paper presentation]. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 130–137). IEEE. https://doi.org/10.1109/ITSC55140.2022.9922440
   Google Scholar
• Wilson, B., Qi, W., Agarwal, T., Lambert, J., Singh, J., Khandelwal, S., Pan, B., Kumar, R., Hartnett, A., Pontes, K. J., Ramanan, D., Carr, P., & Hays, J. (2023). Argoverse 2: Next generation datasets for self-driving perception and forecasting. arXiv preprint arXiv:2301.00493. https://doi.org/10.48550/arXiv.2301.00493
   Google Scholar
• Yang, B., Bai, M., Liang, M., Zeng, W., & Urtasun, R. (2021). Auto4d: Learning to label 4d objects from sequential point clouds. arXiv preprint arXiv:2101.06586. https://doi.org/10.48550/arXiv.2101.06586
   Google Scholar
• Yu, H., Yang, W., Ruan, H., Yang, Z., Tang, Y., Gao, X., Hao, X., Shi, Y., Pan, Y., Sun, N., Song, J., Yuan, J., Luo, P., & Nie, Z. (2023). V2X-Seq: A large-scale sequential dataset for vehicle-infrastructure cooperative perception and forecasting [Paper presentation]. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 5486–5495). https://doi.org/10.1109/CVPR52729.2023.00531
   Google Scholar
• Zhao, J., Yang, X., & Zhang, C. (2023). Vehicle trajectory reconstruction for intersections: An integrated wavelet transform and Savitzky-Golay filter approach. Transportmetrica A: Transport Science. Advance online publication. https://doi.org/10.1080/23249935.2022.2163207
   Web of Science ®Google Scholar
• Zhu, M., Wang, Y., Pu, Z., Hu, J., Wang, X., & Ke, R. (2020). Safe, efficient, and comfortable velocity control based on reinforcement learning for autonomous driving. Transportation Research Part C: Emerging Technologies, 117, 102662. https://doi.org/10.1016/j.trc.2020.102662
   Web of Science ®Google Scholar