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

Paper recorded by Signals 4 on 2026-09-11 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-11 on arXiv · recorded by Signals 4 on 2026-09-14

Category: cs.AI · 人工智能 · first seen 2026-09-14

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 permis

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