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GenAD: Generative End-to-End Autonomous Driving

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arxiv 2402.11502 v3 pith:LY5B77ET submitted 2024-02-18 cs.CV

Wenzhao Zheng , Ruiqi Song , Xianda Guo , Chenming Zhang , Long Chen This is my paper
classification cs.CV
keywords autonomousdrivinggenadend-to-endfuturegenerativelatentmodel
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Directly producing planning results from raw sensors has been a long-desired solution for autonomous driving and has attracted increasing attention recently. Most existing end-to-end autonomous driving methods factorize this problem into perception, motion prediction, and planning. However, we argue that the conventional progressive pipeline still cannot comprehensively model the entire traffic evolution process, e.g., the future interaction between the ego car and other traffic participants and the structural trajectory prior. In this paper, we explore a new paradigm for end-to-end autonomous driving, where the key is to predict how the ego car and the surroundings evolve given past scenes. We propose GenAD, a generative framework that casts autonomous driving into a generative modeling problem. We propose an instance-centric scene tokenizer that first transforms the surrounding scenes into map-aware instance tokens. We then employ a variational autoencoder to learn the future trajectory distribution in a structural latent space for trajectory prior modeling. We further adopt a temporal model to capture the agent and ego movements in the latent space to generate more effective future trajectories. GenAD finally simultaneously performs motion prediction and planning by sampling distributions in the learned structural latent space conditioned on the instance tokens and using the learned temporal model to generate futures. Extensive experiments on the widely used nuScenes benchmark show that the proposed GenAD achieves state-of-the-art performance on vision-centric end-to-end autonomous driving with high efficiency. Code: https://github.com/wzzheng/GenAD.

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Cited by 11 Pith papers

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  3. OmniNWM: Omniscient Driving Navigation World Models

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    OmniNWM jointly generates long panoramic multi-modal driving videos, controls them precisely via normalized Plücker ray-maps, and derives dense driving rewards from generated 3D occupancy.

  4. DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

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    DriveMoE applies scene-specialized Vision MoE and skill-specialized Action MoE to a VLA baseline to achieve SOTA closed-loop performance on Bench2Drive.

  5. ReWorld: Learning Better Representations for World Action Models

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  8. Kerr-Schild Double Copy of the Randall-Sundrum Black String

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  10. OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving

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    A hybrid imitation-plus-reinforcement diffusion planner generates more diverse multi-mode trajectories for end-to-end autonomous driving, with a new diversity metric used for evaluation.

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