Reinforcing End-to-End Autonomous Driving Safety via Negative-Aware Planning in a Unified World-Action Model
Overview
This research presents NegWAM, a safety post-training framework for unified world-action models (WAMs) that learns explicit safety boundaries from near-expert failure trajectories.
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Event evidence and corrections
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Control: reported autonomous
“arXiv:2611.08111v1 Announce Type: new Abstract: World-action models (WAMs) jointly model future traffic states and driving actions for end-to-end autonomous driving in intelligent transportation systems. However, their action learning remains dominated by expert-trajectory imitation, leaving unsafe alternatives close to expert trajectories insufficiently constrained. To improve WAM robustness in safety-critical scena”
Exact source · revision 1Source owner not reported
Reported quantity · EPDMS score on navhard: 40.4 other · Basis not reported
“trajectory compatibility. During training, NegWAM jointly optimizes the flow-matching and near-expert negative objectives, while the auxiliary rider branch is discarded at inference, incurring no additional overhead. On NAVSIM v2, NegWAM achieves 40.4 EPDMS on navhard, a 4.1% relative improvement over the strongest compared scorer-free WAM baseline, and achieves 93.3 EPDMS on navtest. These results demonstrate that N”
Exact source · revision 1Source owner not reported
Reported quantity · EPDMS score on navtest: 93.3 other · Basis not reported
“trajectory compatibility. During training, NegWAM jointly optimizes the flow-matching and near-expert negative objectives, while the auxiliary rider branch is discarded at inference, incurring no additional overhead. On NAVSIM v2, NegWAM achieves 40.4 EPDMS on navhard, a 4.1% relative improvement over the strongest compared scorer-free WAM baseline, and achieves 93.3 EPDMS on navtest. These results demonstrate that N”
Exact source · revision 1Source owner not reported
Artifact availability · code: available
“egWAM achieves strong performance in safety-critical scenarios while preserving routine-driving performance. Zero-shot closed-loop evaluation on HUGSIM further demonstrates cross-benchmark generalization. The code is available at https://github.com/Zizizi-hao/NegWAM.”
Exact source · revision 1Source owner not reported
Evidence dependency changes (1)
- 2026-11-09T21:25:07.331Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalReinforcing End-to-End Autonomous Driving Safety via Negative-Aware Planning in a Unified World-Action Model
This research presents NegWAM, a safety post-training framework for unified world-action models (WAMs) that learns explicit safety boundaries from near-expert failure trajectories.
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