MindDrive: An All-in-One Framework Bridging World Model and Vision-Language Model for End-to-End Autonomous Driving

End-to-end autonomous driving (E2E-AD) has emerged as a new paradigm, where trajectory planning plays a crucial role. Existing studies mainly follow two directions: trajectory generation-oriented approaches, which focuses on pro- ducing high-quality trajectories with simple decision mechanisms, and trajectory selection-oriented approaches, which perform multi-dimensional evaluation to select the best trajectory yet lack sufficient generative capability. In this work, we propose MindDrive, a harmonized framework that integrates high-quality trajectory generation with comprehensive decision reasoning. It establishes a structured reasoning paradigm of “what-if simulation – candidate generation – multi-objective trade-off.” In particular, the proposed Future-aware Trajectory Generator (FaTG), based on a World Action Model (WAM), performs ego-conditioned “what-if” simulations to predict potential future scenes and generate foresighted trajectory candidates. Building upon this, the VLM-oriented Evaluator (VLoE) leverages the reasoning capability of a large vision–language model to conduct multi-objective evaluations across safety, comfort, and efficiency dimensions, leading to reasoned and human-aligned decision- making. Extensive experiments on the NAVSIM-v1 and NAVSIM- v2 benchmarks demonstrate that MindDrive achieves state-of- the-art performance across multi-dimensional driving metrics, significantly enhancing safety, compliance, and generalization, and offering a promising path toward interpretable and cogni- tively guided autonomous driving.