Accelerating Structured Chain-of-Thought in Autonomous Vehicles

• URL: http://arxiv.org/abs/2602.02864v1
• Date: Mon, 02 Feb 2026 22:14:26 GMT
• Title: Accelerating Structured Chain-of-Thought in Autonomous Vehicles
• Abstract summary: Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving.<n>FastDriveCoT is a novel parallel decoding method that accelerates template-structured CoT.
• Score: 55.78214218065611
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Chain-of-Thought (CoT) reasoning enhances the decision-making capabilities of vision-language-action models in autonomous driving, but its autoregressive nature introduces significant inference latency, making it impractical for real-time applications. To address this, we introduce FastDriveCoT, a novel parallel decoding method that accelerates template-structured CoT. Our approach decomposes the reasoning process into a dependency graph of distinct sub-tasks, such as identifying critical objects and summarizing traffic rules, some of which can be generated in parallel. By generating multiple independent reasoning steps concurrently within a single forward pass, we significantly reduce the number of sequential computations. Experiments demonstrate a 3-4$\times$ speedup in CoT generation and a substantial reduction in end-to-end latency across various model architectures, all while preserving the original downstream task improvements brought by incorporating CoT reasoning.

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