[18] Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi. Social gan: Socially acceptable trajectories with generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2255–2264, 2018.
[19] Xin Huang, Guy Rosman, Ashkan Jasour, Stephen G McGill, John J Leonard, and Brian C Williams. Tip: Task-informed motion prediction for intelligent vehicles. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 11432-11439. IEEE, 2022.
[20] Maximilian Igl, Daewoo Kim, Alex Kuefler, Paul Mougin, Punit Shah, Kyriacos Shiarlis, Dragomir Anguelov, Mark Palatucci, Brandyn White, and Shimon Whiteson. Symphony: Learning realistic and diverse agents for autonomous driving simulation. In 2022 International Conference on Robotics and Automation (ICRA), pages 2445-2451. IEEE, 2022.
[21] Boris Ivanovic and Marco Pavone. Injecting planning-awareness into prediction and detection evaluation. In 2022 IEEE Intelligent Vehicles Symposium (IV), pages 821-828. IEEE, 2022.
[22] Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, and Raquel Urtasun. Learning lane graph representations for motion forecasting. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part II 16, pages 541-556. Springer, 2020.
[23] Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, Yun-Pang Flotteröd, Robert Hilbrich, Leonhard Lücken, Johannes Rummel, Peter Wagner, and Evamarie Wießner. Microscopic traffic simulation using sumo. In 2018 21st international conference on intelligent transportation systems (ITSC), pages 2575-2582. IEEE, 2018.
[24] Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, and Adrien Gaidon. Control-aware prediction objectives for autonomous driving. In 2022 International Conference on Robotics and Automation (ICRA), pages 01-08. IEEE, 2022.
[25] Sajjad Mozaffari, Omar Y Al-Jarrah, Mehrdad Dianati, Paul Jennings, and Alexandros Mouzakitis. Deep learning-based vehicle behavior prediction for autonomous driving applications: A review. IEEE Transactions on Intelligent Transportation Systems, 23(1):33-47, 2020.
[26] Andreas Møgelmose, Mohan M. Trivedi, and Thomas B. Moeslund. Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations. In 2015 IEEE Intelligent Vehicles Symposium (IV), pages 330-335, June 2015.
[27] Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, et al. Scene transformer: A unified architecture for predicting future trajectories of multiple agents. In International Conference on Learning Representations, 2021.
[28] Jonah Philion, Amlan Kar, and Sanja Fidler. Learning to evaluate perception models using planner-centric metrics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14055-14064, 2020.
[29] Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M Kitani, Dariu M Gavrila, and Kai O Arras. Human motion trajectory prediction: A survey. The International Journal of Robotics Research, 39(8):895-935, 2020.
[30] Christoph Schöller, Vincent Aravantinos, Florian Lay, and Alois Knoll. What the constant velocity model can teach us about pedestrian motion prediction. IEEE Robotics and Automation Letters, 5(2):1696-1703, 2020.
[31] Marcel Schreiber, Vasileios Belagiannis, Claudius Glaser, and Klaus Dietmayer. Dynamic Occupancy Grid Mapping with Recurrent Neural Networks. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 6717-6724, May 2021.
[32] Adhiraj Somani, Nan Ye, David Hsu, and Wee Sun Lee. Despot: Online pomdp planning with regularization. Advances in neural information processing systems, 26, 2013.