Generating safe and reliable trajectories for autonomous vehicles in long-tail scenarios remains a significant challenge, particularly for high-lateral-acceleration maneuvers such as sharp turns, which represent critical safety situations. Existing trajectory planners exhibit systematic failures in these scenarios due to data imbalance. This results in insufficient modelling of vehicle dynamics, road geometry, and environmental constraints in high-risk situations, leading to suboptimal or unsafe trajectory prediction when vehicles operate near their physical limits. In this paper, we introduce ReflexDiffusion, a novel inference-stage framework that enhances diffusion-based trajectory planners through reflective adjustment. Our method introduces a gradient-based adjustment mechanism during the iterative denoising process: after each standard trajectory update, we compute the gradient between the conditional and unconditional noise predictions to explicitly amplify critical conditioning signals, including road curvature and lateral vehicle dynamics. This amplification enforces strict adherence to physical constraints, particularly improving stability during high-lateral-acceleration maneuvers where precise vehicle-road interaction is paramount. Evaluated on the nuPlan Test14-hard benchmark, ReflexDiffusion achieves a 14.1% improvement in driving score for high-lateral-acceleration scenarios over the state-of-the-art (SOTA) methods. This demonstrates that inference-time trajectory optimization can effectively compensate for training data sparsity by dynamically reinforcing safety-critical constraints near handling limits. The framework's architecture-agnostic design enables direct deployment to existing diffusion-based planners, offering a practical solution for improving autonomous vehicle safety in challenging driving conditions.

翻译：在长尾场景中为自动驾驶车辆生成安全可靠的轨迹仍然是一项重大挑战，尤其是在急转弯等高横向加速度机动这类关键安全情境下。由于数据不平衡，现有的轨迹规划器在这些场景中表现出系统性失效。这导致在高风险情况下对车辆动力学、道路几何和环境约束的建模不足，当车辆运行接近其物理极限时，会产生次优或不安全的轨迹预测。本文提出ReflexDiffusion，一种新颖的推理阶段框架，通过反射调整来增强基于扩散模型的轨迹规划器。我们的方法在迭代去噪过程中引入了一种基于梯度的调整机制：在每次标准轨迹更新后，我们计算条件噪声预测与无条件噪声预测之间的梯度，以显式地放大关键条件信号，包括道路曲率和横向车辆动力学。这种放大强制轨迹严格遵守物理约束，尤其提升了在精确的车-路交互至关重要的高横向加速度机动期间的稳定性。在nuPlan Test14-hard基准测试上的评估表明，ReflexDiffusion在高横向加速度场景下的驾驶分数相比最先进方法提升了14.1%。这证明了推理阶段的轨迹优化能够通过动态强化接近操控极限的安全关键约束，有效补偿训练数据的稀疏性。该框架的架构无关设计使其能够直接部署到现有的基于扩散的规划器中，为在挑战性驾驶条件下提升自动驾驶车辆安全性提供了一个实用解决方案。