aiqbench analysis
This research addresses a critical flaw in reinforcement learning-based driving simulators where fixed action grids cause agents to learn unsafe, abrupt maneuvers that inflate safety metrics artificially. By proposing an adaptive action parameterization that dynamically rediscretizes the control grid based on kinematic constraints, the authors ensure policies remain within human-like comfort bounds without sacrificing navigability. The work also introduces PufferDrive-Editor, a practical tool for auditing realized kinematics and generating challenging training scenarios to validate these improved learning dynamics.
Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies
arXiv:2609.13011v1 Announce Type: cross 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.
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.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2609.13011 [cs.RO]
(or arXiv:2609.13011v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.13011 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 252 words
