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.
Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded. In real driving conditions, camera observations are frequently corrupted by occlusion, motion blur, illumination change, and sensor noise, and when such degraded observations are aggregated indiscriminately over time, trajectory planning becomes unstable and collision risk rises for both the ego vehicle and surrounding road users. Recent Bird's-Eye-View (BEV) approaches unify perception and planning through a shared spatial representation, but most fuse temporal information across frames without assessing the reliability of the underlying observations. We present a Reliable Context-Aware and Temporal Planning framework for Autonomous Driving (RCT-AD) that explicitly models feature quality and temporal consistency to support safer, more consistent planning. A Reliable Context Awareness module scores per-frame reliability and selectively retains trustworthy features through a quality-gated First-In-Last-Out (FILO) memory mechanism, reconstructing degraded observations from reliable historical context so that corrupted inputs do not destabilize the scene representation. A Temporal Trajectory Planner captures long-term dependencies and multi-agent interactions to produce smoother, safety-aware trajectories, while a joint detection-and-segmentation head injects semantic and motion cues into the shared BEV space to strengthen scene understanding. Experiments on the nuScenes autonomous driving benchmark show that RCT-AD improves perception accuracy, motion prediction, and planning robustness over recent end-to-end baselines, achieving 61.5 nuScenes Detection Score, 52.9 mean Average Precision, and 52.3 mean Intersection over Union, while maintaining competitive computational efficiency suitable for real-time deployment.