A MeanFlow-based trajectory planning for CAVs

Abstract

Trajectory planning for connected and automated vehicles requires both scene-aware decision making and multimodal future trajectory generation. However, directly generating trajectories from a single standard noise distribution may lead to insufficient mode separation and reduced candidate diversity. To address this issue, this paper proposes a MeanFlowbased trajectory planning framework that introduces trajectory-cluster-guided Gaussian noise modeling into the generation process. Human driving trajectories are first clustered into multiple behavior modes, and each mode is represented by an individual Gaussian noise distribution. A scene encoder then extracts contextual information from ego-vehicle history, neighboring vehicles, and lane-level features. Conditioned on the scene context and trajectory mode, the MeanFlow decoder generates future trajectory candidates, from which the final trajectory is selected according to the predicted mode probability. Experiments under different mode settings demonstrate that the proposed framework can generate feasible multimodal trajectory candidates and select stable planning outputs for CAV trajectory planning.