PlanVWM: Autonomous Vehicle Planning Method Based on Vectorized World Model Modeling

With the increasing demand for handling complex scenarios in autonomous driving, data-driven planning methods based on imitation learning have attracted significant attention. In this context, this work proposes the PlanVMN method, aiming to enhance the model's robustness to data and its causal inference ability in planning. Based on vectorized scene inputs, this method integrates the world model into the core architecture of the planning model and deduces the temporal features of the scene based on the actions of the ego vehicle. Meanwhile, we introduce an attention-based history enhancement component within the world model, which remarkably improves the planning performance of the ego vehicle. Experiments show that the model has achieved outstanding results in the open-loop and closed-loop tests of nuPlan. Notably, thanks to the generative ability endowed by the unique world model architecture, the model can still exhibit excellent performance when facing blind area, sensor misfuctioning or detection instability, providing strong support for planning in complex scenarios of autonomous driving.