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ManeuverFlow: Maneuver Hypothesis Generation with Future Evidence for Trajectory Evaluation and Refinement in End-to-End Autonomous Driving

Yuhao Wei, Yong Chen, Chen Yang, Yingchen Wang, Delin Ouyang, Lingfeng Qi, Qi Lan, Zhanhua Zhang, Jie Li, Guofa Li
From the abstract

End-to-end planning in complex scenes must model diverse driving intentions and select the most appropriate trajectory using scene context and future risks. However, multi-query regression may collapse to one mode, while anchor-based and generative planners share a scaling bottleneck: small candidate sets limit maneuver coverage or discovery of high-quality trajectories, whereas large sets increase generation and scoring costs.

From the abstract. Our summary is in progress.

The full text is not republished here because the paper's license does not allow it. Read the original on arXiv.