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Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies — arXiv:2609.13011

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.13011
License (as archived): CC0-1.0
Archived: 2026-10-16T03:03:43.674Z
SHA-256 of the archived text: 6e84ea6d5eb7e267d5b884e64cedf59ae67d19cd6154a9a140d65e456fb7ee40
Stable URL of this page: https://metraipolis.com/documento/dc_07bb58d015fc

How to cite

Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies — arXiv:2609.13011 — arXiv (en), https://arxiv.org/abs/2609.13011 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_07bb58d015fc (Metraipolis archive dc_07bb58d015fc, sha256 6e84ea6d5eb7e267d5b884e64cedf59ae67d19cd6154a9a140d65e456fb7ee40)

BibTeX

@misc{dc07bb58d015fc,
  title = {Comfort by Construction: Adaptive, Comfort-Bounded Action Spaces for Learned Driving Policies — arXiv:2609.13011},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.13011},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_07bb58d015fc; sha256 6e84ea6d5eb7e267d5b884e64cedf59ae67d19cd6154a9a140d65e456fb7ee40},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.13011. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.AI).
Categorie arXiv: Robotics (cs.RO) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.13011 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T02:51:27.962Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).