Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives

• URL: http://arxiv.org/abs/2605.19771v1
• Date: Tue, 19 May 2026 12:41:47 GMT
• Title: Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives
• Abstract summary: We propose BeyondDrive, a failure-aware imitation learning framework that jointly learns from successful and failed driving behaviors.<n>First, we introduce a flow matching-based negative trajectory generator that synthesizes safety-critical yet expert-proximate trajectories.<n>Second, we develop a diversity-aware sampling strategy that mitigates mode collapse and improves coverage of diverse failure modes.<n>Third, we propose a Repulsive Distance Loss that simultaneously attracts predictions toward expert demonstrations while repelling them from hard negative trajectories.
• Score: 18.838887346129916
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric deviations from expert trajectories. This paradigm implicitly assumes that spatial proximity implies behavioral safety, leading to a critical objective mismatch: trajectories with nearly identical imitation losses may exhibit drastically different safety outcomes, where one remains recoverable while the other results in collision. To address this limitation, we propose BeyondDrive, a failure-aware imitation learning framework that jointly learns from successful and failed driving behaviors. First, we introduce a flow matching-based negative trajectory generator that synthesizes safety-critical yet expert-proximate trajectories, enabling explicit modeling of safety asymmetry. Second, we develop a diversity-aware sampling strategy that mitigates mode collapse and improves coverage of diverse failure modes during negative trajectory generation. Third, we propose a Repulsive Distance Loss that simultaneously attracts predictions toward expert demonstrations while repelling them from hard negative trajectories, thereby establishing discriminative safety boundaries in trajectory space. Applied to the uni-modal baseline Latent TransFuser, BeyondDrive achieves 89.7 PDMS on the NAVSIMv1 closed-loop benchmark, outperforming prior state-of-the-art methods. Moreover, BeyondDrive generalizes effectively across different autonomous driving architectures, including multi-modal planners, and further demonstrates strong zero-shot transferability on the HUGSIM benchmark.

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