Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies

2026-09-11

AI Summary

该研究通过自适应调整驾驶模拟器中的动作空间，解决了因突然操作导致的舒适度问题，确保学习到的驾驶策略在保证安全的同时提高乘坐舒适度。

Abstract

Data-driven driving simulators command accelerations and steering rates from a fixed grid without constraining the realized accelerations and jerks. As a result, reinforcement-learning policies inflate safety metrics through abrupt, last-second maneuvers that lie far outside the range of human driving and would be unacceptable to occupants of a real vehicle, so the metrics measure simulator permissiveness rather than policy quality. Enforcing comfort bounds naively is not enough: lateral limits shrink quadratically with speed, so clamping a static grid saturates it and destroys fine-grained control ("grid collapse"). We propose an adaptive action parameterization that rediscretizes the grid at every step to span exactly the per-step feasible control set, via closed-form inversion of the lateral-jerk constraint. We further present PufferDrive-Editor, a browser-based tool to audit realized kinematics and author kinematically challenging scenes. On the Waymo Open Motion Dataset and a hand-authored slalom, our adaptive model holds comfort violations below 1% while outperforming clipped-grid and direct-jerk baselines in navigability.

Problem

• data-driven driving simulators
• reinforcement-learning policies
• safety metrics
• comfort bounds
• control precision

Innovation

• adaptive action parameterization
• lateral-jerk constraint
• PufferDrive-Editor