提升自动驾驶碰撞预测的稳定性与鲁棒性，应对微小干扰导致的误判问题。
SECURE: Stable Early Collision Understanding via Robust Embeddings in Autonomous Driving
- 通过多目标损失函数优化模型，增强预测与特征空间的稳定性。
- 在DAD和CCD数据集上，对扰动的鲁棒性显著提升，且干净数据表现更优。
- 适合关注自动驾驶安全系统可靠性的研究者与工程团队。
尽管深度学习大幅推动了事故预判技术发展，但这些关键安全系统在面对真实世界扰动时的鲁棒性仍是重大挑战。我们发现，如CRASH等先进模型虽性能优异，但在遭遇微小输入扰动时，其预测结果和潜在表征表现出显著不稳定性，带来严重可靠性风险。为此，我们提出SECURE——一种基于稳定早期碰撞理解的鲁棒嵌入框架，正式定义并强制实施模型鲁棒性。该框架基于四个核心属性：预测空间与潜在特征空间的一致性与稳定性。我们提出一种原则性训练方法，通过多目标损失函数微调基线模型，最小化与参考模型的差异，并惩罚对抗扰动下的敏感性。在DAD与CCD数据集上的实验表明，该方法不仅显著提升对各类扰动的鲁棒性，还改善了干净数据上的性能，达到新的最优水平。
原文摘要 · Abstract (English)
While deep learning has significantly advanced accident anticipation, the robustness of these safety-critical systems against real-world perturbations remains a major challenge. We reveal that state-of-the-art models like CRASH, despite their high performance, exhibit significant instability in predictions and latent representations when faced with minor input perturbations, posing serious reliability risks. To address this, we introduce SECURE - Stable Early Collision Understanding Robust Embeddings, a framework that formally defines and enforces model robustness. SECURE is founded on four key attributes: consistency and stability in both prediction space and latent feature space. We propose a principled training methodology that fine-tunes a baseline model using a multi-objective loss, which minimizes divergence from a reference model and penalizes sensitivity to adversarial perturbations. Experiments on DAD and CCD datasets demonstrate that our approach not only significantly enhances robustness against various perturbations but also improves performance on clean data, achieving new state-of-the-art results.
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