A Semi-decentralized and Variational-Equilibrium-Based Trajectory Planner for Connected and Autonomous Vehicles

TL;DR

This paper introduces a semi-decentralized trajectory planning method for connected and autonomous vehicles that leverages variational equilibrium concepts to improve safety and computational efficiency using V2X technology.

Contribution

It formulates vehicle trajectory planning as a game with safety constraints and proposes a novel VE-based semi-decentralized planner for better scalability and safety.

Findings

Fast computation speed demonstrated in experiments

High scalability of the proposed method

Enhanced safety through equilibrium concordance

Abstract

This paper designs a novel trajectory planning approach to resolve the computational efficiency and safety problems in uncoordinated methods by exploiting vehicle-to-everything (V2X) technology. The trajectory planning for connected and autonomous vehicles (CAVs) is formulated as a game with coupled safety constraints. We then define interaction-fair trajectories and prove that they correspond to the variational equilibrium (VE) of this game. We propose a semi-decentralized planner for the vehicles to seek VE-based fair trajectories, which can significantly improve computational efficiency through parallel computing among CAVs and enhance the safety of planned trajectories by ensuring equilibrium concordance among CAVs. Finally, experimental results show the advantages of the approach, including fast computation speed, high scalability, equilibrium concordance, and safety.