让AI开车前，先让它看懂路
现在的自动驾驶模型大多是“黑盒”:它知道怎么打方向盘，但你说不清它到底“看没看懂”路况。Qwen-Drive-1.0 把“看懂”和“开车”装进同一个模型：它用鸟瞰视角的感知头，把3D物体检测、路面占用、地图分割这些“视觉理解”显式地画出来，再让一个规划模块基于这些理解生成行驶轨迹。训练分两步走，先喂通用视觉数据保住它“什么都能看”的能力，再叠加驾驶数据学开车。结果是在3D感知和场景理解上很强，同时没丢掉通用对话能力，规划表现也达到一流水平。它不是你明天能开上的车，但它指了个方向：让自动驾驶从“会开”走向“懂路”。
📄 原文摘要(英文)
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and integrates 3D perception, visual question answering, and motion planning within a unified framework. An external bird's-eye-view (BEV) perception head jointly performs 3D object detection, semantic occupancy prediction, and BEV map segmentation. It serves as a probe of the 3D information accessible from the shared representations and provides an explicit, inspectable interface to 3D scene structure. A Planning Expert conditions on shared VLM representations to generate future ego trajectories. A staged training recipe combines driving supervision with general-purpose vision-language data to acquire driving-specific competence while helping preserve broad visual understanding and instruction-following capabilities. Experiments demonstrate strong 3D perception and driving scene understanding while largely preserving general vision-language capability. Comprehensive evaluations across open-loop, pseudo-closed-loop, and closed-loop settings further show highly competitive motion-planning performance.