MindDrive: An All-in-One Framework Bridging World Models and Vision-Language Model for End-to-End Autonomous Driving
Classification
View four quadrants- Major category
- WAMs
- Quadrant
- Outside quadrants
- Architecture
- Pending verification
- Prediction paradigm
- Other mechanisms
- Subcategories
- Autonomous driving
- Source review status
- Not assigned
Category review. An action-conditioned BEV world model predicts future context for a separate anchor decoder and VLM scorer that refine and select driving trajectories. The described action mechanism is external planning, model-assisted policy optimization, geometric tracking, or video-conditioned control; the source does not establish joint future/action generation or an IDM action decoder. Reading evidence
Contribution
MindDrive couples action-conditioned BEV scene prediction with anchor refinement and a VLM trajectory scorer. Its main contribution is the connection between future-aware candidate generation and selection. NAVSIM scores support planning gains, while incomplete training specifications and inconsistent result reporting limit reproducibility.
Abstract
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Affiliations
School of Transportation Science and Engineering, Beihang University; State Key Laboratory of Intelligent Transportation System, Beihang University; Hangzhou International Innovation Institute, Beihang University; Contemporary Amperex Technology Co., Limited (CATL); Research Institute of Aero-Engine, Beihang University; School of Computer Science and Technology, Beijing Jiaotong University; China Automotive Engineering Research Institute Co., Ltd.