Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction

Lane topology extraction involves detecting lanes and traffic elements and determining their relationships, a key perception task for mapless autonomous driving. This task requires complex reasoning, such as determining whether it is possible to turn left into a specific lane. To address this challenge, we introduce neuro-symbolic methods powered by vision-language foundation models (VLMs). Existing approaches have notable limitations: (1) Dense visual prompting with VLMs can achieve strong performance but is costly in terms of both financial resources and carbon footprint, making it impractical for robotics applications. (2) Neuro-symbolic reasoning methods for 3D scene understanding fail to integrate visual inputs when synthesizing programs, making them ineffective in handling complex corner cases. To this end, we propose a fast-slow neuro-symbolic lane topology extraction algorithm, named Chameleon, which alternates between a fast system that directly reasons over detected instances using synthesized programs and a slow system that utilizes a VLM with a chain-of-thought design to handle corner cases. Chameleon leverages the strengths of both approaches, providing an affordable solution while maintaining high performance. We evaluate the method on the OpenLane-V2 dataset, showing consistent improvements across various baseline detectors. Our code, data, and models are publicly available atthis https URL

@article{zhang2025_2503.07485,
  title={ Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction },
  author={ Zongzheng Zhang and Xinrun Li and Sizhe Zou and Guoxuan Chi and Siqi Li and Xuchong Qiu and Guoliang Wang and Guantian Zheng and Leichen Wang and Hang Zhao and Hao Zhao },
  journal={arXiv preprint arXiv:2503.07485},
  year={ 2025 }
}