VLG-Drive: Unifying Vision-Language and Geometric Priors for Autonomous Driving

A driving planner needs both scene semantics, which vision-language models (VLMs) provide, and spatial structure, which geometric foundation models recover from visual history. We present VLG-Drive, which keeps both models frozen and trains only a fusion interface and a planner from trajectory labels. At each camera-grid cell, the interface fuses the paired VLM and geometry features into one joint token through a geometry-anchored residual, and a command-conditioned planner reads these tokens to predict the ego trajectory. VLG-Drive achieves 31.78 EPDMS on NavHard under perturbed initial states, the highest among the compared methods, and 88.27 PDMS on NavTest from cameras alone. Together with consistent gains over independent VLM and geometry streams, these results show that planning directly from the fused features of vision-language and geometric foundation models opens a new direction for building driving planners.