EdgePlanner: An Edge-Assisted Agentic Framework for Autonomous Vehicle Trajectory Planning via V2X Collaboration

Abstract

Vehicle-to-everything (V2X) techniques expand the capability boundaries of connected and autonomous vehicles (CAVs). However, deploying V2X-enabled end-to-end autonomous driving (E2E-AD) systems still faces a trade-off between sharing high-resolution perception features and V2X communication bandwidth constraints. Furthermore, complex E2E models impose heavy computational overhead and inference latency on CAVs. To address these challenges, we propose EdgePlanner, an agentic V2X collaborative framework for CAV trajectory planning. EdgePlanner decouples conventional vehicle-centric E2E-AD into role-specialized on-board and edge agents, assigning explicit decision roles within a closed-loop planning workflow. The on-board agent is dedicated to preliminary perception processing. It employs clustering-based feature compression and temporal difference transmission modules to minimize bandwidth consumption while preserving critical semantic information. Conversely, the edge agent serves as a global planner that integrates collaborative V2X information from multiple CAVs, roadside infrastructure, and map context. It adopts an Agent Query module to capture complex interactions and generates highly reliable trajectories for on-board agents with a conditioned Denoising Diffusion Implicit Model (DDIM) decoder. Extensive experiments demonstrate that EdgePlanner consistently generates high-quality trajectories for CAVs, while significantly reducing communication overhead.