Neuro-Symbolic Drive: Rule-Grounded Faithful Reasoning for Driving VLAs

Neuro-Symbolic Drive introduces a novel framework for driving Vision-Language Agents (VLAs) that integrates rule-grounded reasoning from classical planners with Chain-of-Thought (CoT) reasoning. By fine-tuning the Qwen3.5-4B model using structured reasoning traces from rule-based planners, the framework achieves significant performance improvements on a simulator-generated benchmark, reducing Average Deviation Error (ADE) from 0.47 to 0.26 and miss rates from 8.30% to 6.40% with three-camera perception. This approach enhances the causal connection between reasoning and motion generation, offering a structured supervision method that could benefit practitioners in developing more reliable and interpretable driving AI systems.