Collision risk estimation and avoidance play central roles in the safety of autonomous driving (AD) systems. Recently emerged end-to-end AD systems gain collision avoidance ability by minimizing losses to penalize planning trajectories that are too close to other objects. Despite a significant collision rate during testing, most end-to-end planners do not explicitly quantify the collision risk in their outputs. To address this, we introduce RiskMonitor, an efficient plug-and-play module that interprets planning and motion tokens from state-of-the-art end-to-end planners to estimate collision risk. Inspired by loss prediction based uncertainty quantification, RiskMonitor predicts whether the collision loss -- commonly adopted to train end-to-end planners -- is positive along planned waypoints, framing collision risk estimation as a binary classification task. We evaluate RiskMonitor on the real-world nuScenes dataset (open-loop) and the neural-rendering based simulator, NeuroNCAP (closed-loop). Our token-driven method outperforms prediction-driven approaches, including deterministic rules, Gaussian mixture models, and Monte Carlo Dropout. When integrated with a simple braking policy, RiskMonitor improves collision avoidance ability by $66.5\%$ in a closed-loop test on safety-critical scenarios. These results demonstrate that monitoring collision risk using plan and motion tokens enhances the safety of end-to-end AD without retraining it.

翻译：碰撞风险估计与避碰在自动驾驶系统的安全性中起着核心作用。近期兴起的端到端自动驾驶系统通过最小化损失来惩罚过于接近其他物体的规划轨迹，从而获得避碰能力。尽管在测试中存在显著的碰撞率，大多数端到端规划器并未在其输出中明确量化碰撞风险。为解决此问题，我们提出了RiskMonitor，这是一个高效的即插即用模块，通过解读最先进端到端规划器输出的规划与运动token来估计碰撞风险。受基于损失预测的不确定性量化方法启发，RiskMonitor预测沿规划路径点的碰撞损失（通常用于训练端到端规划器）是否为正，从而将碰撞风险估计构建为一个二元分类任务。我们在真实世界nuScenes数据集（开环）和基于神经渲染的仿真器NeuroNCAP（闭环）上评估RiskMonitor。我们的token驱动方法优于预测驱动方法，包括确定性规则、高斯混合模型和蒙特卡洛Dropout。当与简单的制动策略集成时，RiskMonitor在安全关键场景的闭环测试中将避碰能力提升了$66.5\%$。这些结果表明，利用规划与运动token监控碰撞风险能够在不重新训练的情况下提升端到端自动驾驶系统的安全性。