Qwen-Drive 1.0: Turning a General VLM into a Driving Foundation Model

Qwen-Drive 1.0: Turning a General VLM into a Driving Foundation Model Paper Code Project The most recent Vision-Language-Action approaches for autonomous driving use a pretrained VLM mainly as a source of semantic understanding and reasoning, then specialize it on driving data and connect it to an action or trajectory predictor. Qwen-Drive-1.0 asks a somewhat broader question: can a general-purpose VLM be turned into a foundation model for driving without giving up the capabilities that made the original VLM useful? This matters because language supervision alone is a poor constraint on actual 3D geometry (a model may describe a scene convincingly while having only an approximate understanding of distances and occupancy) and aggressive domain adaptation can destroy some of the general visual and world knowledge that may be particularly valuable in rare or out-of-distribution situations. Qwen-Drive addresses this by keeping the architecture of Qwen3.5-4B intact while attaching two…