ReflexDiffusion: Reflection-Enhanced Trajectory Planning for High-lateral-acceleration Scenarios in Autonomous Driving

Abstract 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 that represent critical safety situations. Existing trajectory planners exhibit systematic failures in these scenarios due to data imbalance, resulting in insufficient representation 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 boundaries. 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 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 compared to state-of-the-art methods. This demonstrates that inference-time trajectory optimization can effectively compensate for training data sparsity by dynamically reinforcing safety-critical constraints at the handling limits. The framework's architecture-agnostic design enables direct deployment across existing diffusion-based planners, offering a practical solution for improving autonomous vehicle safety in challenging driving conditions.