Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs
Driving VLA models incorporating Chain-of-Thought (CoT) reasoning are attractive because they leverage pretrained VLM representations and expose intermediate decisions in natural language, yet current rationales often lack the step-by-step decision semantics needed to keep the rationale causally connected to the planned motion. We introduce Neuro-Symbolic Drive, a neuro-symbolic driving framework that supervises a driving VLA with rule-grounded reasoning traces extracted directly from classical rule-based planners. Our key observation is that rule-based planners are symbolic AI systems that already function as executable reasoning engines: they reason about active safety constraints, sear...
| Authors | Xiangbo Gao, Xiukun Huang, Boyu Lu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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