Qwen-Drive 1.0 tells you why it brakes, just don't expect the explanation to match the maneuver

Alibaba's Qwen-Drive 1.0 adds 3D perception and planning modules to Qwen3.5-4B for driving tasks, though explanations often mismatch maneuvers.

Qwen-Drive 1.0, built on Qwen3.5-4B, combines spatial perception, traffic question answering, and route planning in one vision-language model, adding a bird's-eye-view perception module and a Planning Expert trained via staged fine-tuning and reinforcement learning. The paper finds text-image models do not inherently grasp 3D space; spatial accuracy only improved when the base vision-language model itself was trained on spatial tasks, while avoiding catastrophic forgetting of general knowledge. The cut reinforcement learning-trained version halved road-departure rate in simulation from 24% to 12%, and the model beats specialized driving models in most of Qwen's benchmarks, but its explanations sometimes conflate causes like distant red lights and crossing children, and results partly rest on self-designed tests. The work follows prior findings from PaLM-E and a UC Santa Cruz adversarial sign attack on DriveLM showing VLM driving models' reasoning and spatial gaps.