[25] J. Amirian, B. Zhang, F. V. Castro, J. J. Baldelomar, J.-B. Hayet, and J. Pettré, “OpenTraj: Assessing prediction complexity in human trajectories datasets,” in Asian Conference on Computer Vision, 2020.
[26] A. Malinin, N. Band, Y. Gal, M. Gales, A. Ganshin, G. Chesnokov, A. Noskov, A. Ploskonosov, L. Prokhorenkova, I. Provilkov, V. Raina, V. Raina, D. Roginskiy, M. Shmatova, P. Tiges, and B. Yangel, “Shifts: A dataset of real distributional shift across multiple large-scale tasks,” in Conf. on Neural Information Processing Systems Datasets and Benchmarks Track, 2021. [Online]. Available: https://openreview.net/forum?id=qM45LHaWM6E
[27] H. Caesar, J. Kabzan, K. S. Tan, W. K. Fong, E. Wolff, A. Lang, L. Fletcher, O. Beijbom, and S. Omari, "nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles," 2021, Available at https://arxiv.org/abs/2106.11810.
[28] P. Kothari, S. Kreiss, and A. Alahi, "Human trajectory forecasting in crowds: A deep learning perspective," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7386-7400, 2022.
[29] A. Rudenko, L. Palmieri, W. Huang, A. J. Lilienthal, and K. O. Arras, "The atlas benchmark: An automated evaluation framework for human motion prediction," in IEEE Int. Conf. on Robot and Human Interactive Communication, 2022.
[30] A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, "Automatic differentiation in PyTorch," in Conf. on Neural Information Processing Systems - Autodiff Workshop, 2017.
[31] D. Rempe, Z. Luo, X. B. Peng, Y. Yuan, K. Kitani, K. Kreis, S. Fidler, and O. Litany, "Trace and Pace: Controllable pedestrian animation via guided trajectory diffusion," in IEEE Conf. on Computer Vision and Pattern Recognition, 2023.
[32] D. Xu, Y. Chen, B. Ivanovic, and M. Pavone, "BITS: Bi-level imitation for traffic simulation," in IEEE Int. Conf. on Robotics and Automation, 2023.
[33] Z. Zhong, D. Rempe, D. Xu, Y. Chen, S. Veer, T. Che, B. Ray, and M. Pavone, "Guided conditional diffusion for controllable traffic simulation," in IEEE Int. Conf. on Robotics and Automation, 2023.
[34] F. Christianos, P. Karkus, B. Ivanovic, S. V. Albrecht, and M. Pavone, "Planning with occluded traffic agents using bi-level variational occlusion models," in IEEE Int. Conf. on Robotics and Automation, 2023.
[35] Y. Chen, P. Karkus, B. Ivanovic, X. Weng, and M. Pavone, "Tree-structured policy planning with learned behavior models," in IEEE Int. Conf. on Robotics and Automation, 2023.
[36] The Apache Software Foundation, "Apache arrow," 2023, Available at https://github.com/apache/arrow
[37] S. Gillies, C. van der Wel, J. Van den Bossche, M. W. Taves, J. Arnott, B. C. Ward, and others, "Shapely," 2023, Available at https://github.com/shapely/shapely.
[38] Google Inc., "Protocol buffers - google's data interchange format," 2023, Available at https://github.com/protocolBuffers/protobuf
[39] W. Zhan, L. Sun, D. Wang, H. Shi, A. Clausse, M. Naumann, J. Kümmerle, H. Königshof, C. Stiller, A. de La Fortelle, and M. Tomizuka, “INTERACTION Dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps,” 2019, Available at https://arxiv.org/abs/1910.03088.
[40] A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, "Learning social etiquette: Human trajectory prediction in crowded scenes," in European Conf. on Computer Vision, 2016.
[41] G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba. (2016) OpenAI Gym. Available at https://arxiv.org/abs/1606.01540.