Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

B. Ivanovic, A. Elhafsi, G. Rosman, A. Gaidon, and M. Pavone, “MATS: An interpretable trajectory forecasting representation for planning and control,” arXiv preprint arXiv:2009.07517 , 2020

Original

2020

Earlier work this paper cites.

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020

2020

Earlier work this paper cites.

Z. Huang, J. Wu, and C. Lv, “Driving behavior modeling using naturalistic human driving data with inverse reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems , 2021

2021

Earlier work this paper cites.

A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International conference on machine learning . PMLR, 2021, pp. 8162–8171

2021

Earlier work this paper cites.

Z. Huang, X. Mo, and C. Lv, “Multi-modal motion prediction with transformer-based neural network for autonomous driving,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2605–2611

2022

Earlier work this paper cites.

M. Janner, Y. Du, J. B. Tenenbaum, and S. Levine, “Planning with diffusion for flexible behavior synthesis,” arXiv preprint arXiv:2205.09991 , 2022

Original

2022

Earlier work this paper cites.

J. Abramson, A. Ahuja, F. Carnevale, P. Georgiev, A. Goldin, A. Hung, J. Landon, J. Lhotka, T. Lillicrap, A. Muldal, et al. , “Improving multimodal interactive agents with reinforcement learning from human feedback,” arXiv preprint arXiv:2211.11602 , 2022

Original

2022

Earlier work this paper cites.

Z. Huang, H. Liu, and C. Lv, “Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 3903–3913

2023

Earlier work this paper cites.

Y. Chen, S. Tonkens, and M. Pavone, “Categorical traffic transformer: Interpretable and diverse behavior prediction with tokenized latent,” arXiv preprint arXiv:2311.18307 , 2023

Original

2023

Earlier work this paper cites.

Y. Chen, S. Veer, P. Karkus, and M. Pavone, “Interactive joint planning for autonomous vehicles,” IEEE Robotics and Automation Letters , 2023

2023

Earlier work this paper cites.

Z. Huang, H. Liu, J. Wu, and C. Lv, “Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving,” IEEE Transactions on Neural Networks and Learning Systems , 2023

2023

Earlier work this paper cites.

P. Karkus, B. Ivanovic, S. Mannor, and M. Pavone, “Diffstack: A differentiable and modular control stack for autonomous vehicles,” in Conference on robot learning . PMLR, 2023, pp. 2170–2180

2023

Earlier work this paper cites.

Z. Huang, H. Liu, J. Wu, and C. Lv, “Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , 2023

2023

Earlier work this paper cites.

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 2023 IEEE International Conference on Robotics and Automation (ICRA) , 2023, pp. 3560–3566

2023

Earlier work this paper cites.

Z. Zhong, D. Rempe, Y. Chen, B. Ivanovic, Y. Cao, D. Xu, M. Pavone, and B. Ray, “Language-guided traffic simulation via scene-level diffusion,” in Conference on Robot Learning . PMLR, 2023, pp. 144–177

2023

Cited alongside, same era.

C. Jiang, A. Cornman, C. Park, B. Sapp, Y. Zhou, D. Anguelov, et al. , “Motiondiffuser: Controllable multi-agent motion prediction using diffusion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9644–9653

2023

Cited alongside, same era.

C. Xu, D. Zhao, A. Sangiovanni-Vincentelli, and B. Li, “Diffscene: Diffusion-based safety-critical scenario generation for autonomous vehicles,” in The Second Workshop on New Frontiers in Adversarial Machine Learning , 2023

2023

Cited alongside, same era.

Z. Zhou, J. Wang, Y.-H. Li, and Y.-K. Huang, “Query-centric trajectory prediction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 863–17 873

2023

Z. Huang, P. Karkus, B. Ivanovic, Y. Chen, M. Pavone, and C. Lv, “DTPP: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 6806–6812

2024

Closest in time.

Z. Huang, Z. Zhang, A. Vaidya, Y. Chen, C. Lv, and J. F. Fisac, “Versatile scene-consistent traffic scenario generation as optimization with diffusion,” arXiv preprint arXiv:2404.02524 , 2024

Original

2024

Closest in time.

M. Uehara, Y. Zhao, T. Biancalani, and S. Levine, “Understanding reinforcement learning-based fine-tuning of diffusion models: A tutorial and review,” arXiv preprint arXiv:2407.13734 , 2024

Original

2024

Closest in time.

N. Karnchanachari, D. Geromichalos, K. S. Tan, N. Li, C. Eriksen, S. Yaghoubi, N. Mehdipour, G. Bernasconi, W. K. Fong, Y. Guo, et al. , “Towards learning-based planning: The nuplan benchmark for real-world autonomous driving,” arXiv preprint arXiv:2403.04133 , 2024

Original