Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving

Key points

Without changing the architecture of the pretrained VLM (Qwen3.5-4B), it added just two external modules, a BEV perception head and a Planning Expert, to integrate 3D perception, question answering, and path planning.
It used the BEV head as a '3D probe' to make it possible to test how well the model understands actual 3D scene structure, beyond simply producing fluent text answers.
Staged training that mixes driving data with general-purpose vision-language data preserved both driving and general capabilities, mitigating catastrophic forgetting (losing old capabilities while learning new ones).
It achieved 43.95 mAP for 3D perception and 60.99 mIoU for mapping on nuScenes, a NAVSIM driving score (PDMS) of 90.7, and a Waymo E2E evaluator score of 7.91.
In line with the trend toward vehicle cockpits (multimedia) and driving functions sharing a single computing platform, it aims for a model that handles them together rather than a model dedicated only to driving.

External research summaries. These are not HDATF publications or measured product results.