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arXiv Computer Vision By Zihao Zhang, Xinyu Zhang, Bing Zeng, Xingyu Chen, Zhenning Li, Bin Yu, Ziyuan Pu

Reinforcing End-to-End Autonomous Driving Safety via Negative-Aware Planning in a Unified World-Action Model

Read the original on arXiv Computer Vision →

The paper introduces NegWAM, a safety post‑training framework for unified world‑action models (WAMs) used in end‑to‑end autonomous driving. NegWAM generates negative trajectories that are close to expert paths but contain safety risks, using them as repulsive anchors to steer predictions away from dangerous behavior while encoding scene‑trajectory compatibility via rider tokens. The method achieves a 4.1% relative improvement on the NAVSIM v2 navhard benchmark and 93.3 EPDMS on navtest, demonstrating strong safety performance without sacrificing routine driving, and shows cross‑benchmark generalization in zero‑shot closed‑loop tests on HUGSIM.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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