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Explicit Geometric Chain-of-Thought for Vision-Language-Action in Autonomous Driving

Paper recorded by Signals 4 on 2026-10-07 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-07 on arXiv · recorded by Signals 4 on 2026-10-08

Category: cs.CV · 计算机视觉 · first seen 2026-10-08

Abstract

Vision-language-action~(VLA) models have emerged as a promising paradigm for autonomous driving. However, existing VLA models still suffer from a fundamental mismatch: driving actions require precise 3D geometric cues, while visual-language understanding and reasoning are largely conducted in a 2D semantic space. In this paper, we propose GeoCoTDrive, an explicit geometric chain-of-thought framewo

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#334 most recent of 756 cs.CV papers we have recorded · ↑ newer: MOTIP2: Spatial Priors for End-to-End Multi-Object Tracking · ↓ older: World Models' Last Exam in Physics
Cite this page: Explicit Geometric Chain-of-Thought for Vision-Language-Action in Autonomous Driving: the #334 most recent of 756 cs.CV papers we have recorded (as of 2026-10-07). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/explicit-geometric-chain-of-thought-for-vision-language-action-in-autonomous-dri.html
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