[13] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021.
[14] Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet. Video diffusion models. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.
[15] WuLing Huang, Kunfeng Wang, Yisheng Lv, and FengHua Zhu. Autonomous vehicles testing methods review. In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pages 163-168. IEEE, 2016.
[16] Aapo Hyvarinen and Peter Dayan. Estimation of non-normalized statistical models by score matching. Journal of Machine Learning Research, 6(4), 2005.
[17] Ashesh Jain, Luca Del Pero, Hugo Grimmett, and Peter Ondruska. Autonomy 2.0: Why is self-driving always 5 years away? arXiv preprint arXiv:2107.08142, 2021.
[18] Faris Janjos, Maxim Dolgov, Muhamed Kurić, Yinzhe Shen, and J Marius Zöllner. San: Scene anchor networks for joint action-space prediction. In 2022 IEEE Intelligent Vehicles Symposium (IV), pages 1751-1756. IEEE, 2022.
[19] Faris Janjos, Maxim Dolgov, and J Marius Zöllner. Starnet: Joint action-space prediction with star graphs and implicit global frame self-attention. arXiv preprint arXiv:2111.13566, 2021.
[20] Michael Janner, Yilun Du, Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference on Machine Learning, 2022.
[21] Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion-based generative models. In Advances in Neural Information Processing Systems, 2022.
[22] Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro. Diffwave: A versatile diffusion model for audio synthesis. In International Conference on Learning Representations, 2021.
[23] Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker. Desire: Distant future prediction in dynamic scenes with interacting agents. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 336-345, 2017.
[24] Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, and Raquel Urtasun. Learning lane graph representations for motion forecasting. In European Conference on Computer Vision, pages 541-556. Springer, 2020.
[25] Yicheng Liu, Jinghuai Zhang, Liangji Fang, Qinhong Jiang, and Bolei Zhou. Multimodal motion prediction with stacked transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7577-7586, 2021.
[26] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations, 2021.
[27] Jean Mercat, Thomas Gilles, Nicole El Zoghby, Guillaume Sandou, Dominique Beauvois, and Guillermo Pita Gil. Multi-head attention for multi-modal joint vehicle motion forecasting. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 9638-9644. IEEE, 2020.
[28] Xiaoyu Mo, Yang Xing, and Chen Lv. Recog: A deep learning framework with heterogeneous graph for interaction-aware trajectory prediction. arXiv preprint arXiv:2012.05032, 2020.
[29] Nigamaa Nayakanti, Rami Al-Rfou, Aurick Zhou, Kratarth Goel, Khaled S Refaat, and Benjamin Sapp. Wayformer: Motion forecasting via simple & efficient attention networks. arXiv preprint arXiv:2207.05844, 2022.
[30] Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David J Weiss, Ben Sapp, Zhifeng Chen, and Jonathon Shlens. Scene transformer: A unified architecture for predicting future trajectories of multiple agents. In International Conference on Learning Representations, 2022.
[31] Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. Zero-shot image-to-image translation. arXiv preprint arXiv:2302.03027, 2023.
[32] Tung Phan-Minh, Elena Corina Grigore, Freddy A Boulton, Oscar Beijbom, and Eric M Wolff. Covernet: Multimodal behavior prediction using trajectory sets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14074-14083, 2020.
[33] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652-660, 2017.
[34] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.
[35] Davis Rempe, Zhengyi Luo, Xue Bin Peng, Ye Yuan, Kris Kitani, Karsten Kreis, Sanja Fidler, and Or Litany. Trace and pace: Controllable pedestrian animation via guided trajectory diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13756-13766, 2023.
[36] Nicholas Rhinehart, Rowan McAllister, Kris Kitani, and Sergey Levine. Precog: Prediction conditioned on goals in visual multi-agent settings. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2821-2830, 2019.