Accelerating Time-Optimal Trajectory Planning for Connected and Automated Vehicles with Graph Neural Networks

TL;DR

This paper introduces a graph neural network-based framework that significantly accelerates time- and energy-optimal trajectory planning for connected and automated vehicles by providing warm-start solutions for real-time optimization.

Contribution

The paper presents a novel GNN-based approach that learns to generate warm-start solutions, enabling faster trajectory planning without sacrificing optimality in CAVs.

Findings

Reduces computation time for trajectory planning.

Maintains control performance while accelerating planning.

Enables real-time multi-agent coordination in traffic scenarios.

Abstract

In this paper, we present a learning-based framework that accelerates time- and energy-optimal trajectory planning for connected and automated vehicles (CAVs) using graph neural networks (GNNs). We formulate the multi-agent coordination problem encountered in traffic scenarios as a cooperative trajectory planning problem that minimizes travel time, subject to motion primitives derived from energy-optimal solutions. The performance of this framework can be further improved through replanning at each time step, enabling the system to incorporate newly observed information. To achieve real-time execution, we employ a graph isomorphism network with edge features (GINEConv) to learn the solutions of the time-optimal trajectory planning problem from offline-generated data. The trained model produces online predictions that serve as warm-starts for numerical optimization, thereby enabling rapid computation of minimal exit times and the associated feasible trajectories. This learning-to-warm-start approach substantially reduces computation time while preserving the control performance of the time- and energy-optimal trajectory planning framework.