Qwen-Drive-1.0-4B

> [!Note] > This repository contains the weights and configurations of Qwen-Drive-1.0 in the Hugging > Face format. The accompanying code, demo data and documentation are released at > QwenLM/Qwen-Drive-1.0. Qwen-Drive-1.0 retains the architecture of the pretrained Qwen3.5 vision-language model and integrates **3D perception**, **visual question answering**, and **motion planning** within a unified framework. The natively multimodal Qwen3.5-4B serves as the shared VLM, with two external modules attached: a **BEV perception head** jointly performing 3D object detection, semantic occupancy prediction and BEV map segmentation, and a **Planning Expert** that conditions on the shared VLM representations to generate future ego trajectories through flow matching. The unchanged VLM answers free-form questions about driving scenes. A staged training recipe combines driving supervision with general-purpose vision-language data, so the model acquires driving-specific competence while preserving broad visual understanding and instruction-following capability. With planning samples assembled purely from public sources, Qwen-Drive-1.0 unifies the trajectory format across datasets and evaluates from open-loop prediction to closed-loop driving. The SFT model is already competitive across all benchmarks. After reinforcement learning, the model trades only a marginal open-loop displacement for comprehensive gains in human-preference alignment and closed-loop safety.