Robust Learning-Based Trajectory Planning for Emerging Mobility Systems

TL;DR

This paper presents a robust trajectory planning framework for connected and automated vehicles at intersections, incorporating uncertainty via Gaussian process regression to ensure safety constraints are met despite noisy observations.

Contribution

It extends existing CAV coordination frameworks by integrating online uncertainty estimation and robust optimization, enhancing safety and reliability.

Findings

Framework successfully accounts for trajectory deviations

Simulation shows improved safety margins

Robust planning maintains constraints under uncertainty

Abstract

In this paper, we extend a framework that we developed earlier for coordination of connected and automated vehicles (CAVs) at a signal-free intersection to incorporate uncertainty. Using the possibly noisy observations of actual time trajectories and leveraging Gaussian process regression, we learn the bounded confidence intervals for deviations from the nominal trajectories of CAVs online. Incorporating these confidence intervals, we reformulate the trajectory planning as a robust coordination problem, the solution of which guarantees that constraints in the system are satisfied in the presence of bounded deviations from the nominal trajectories. We demonstrate the effectiveness of our extended framework through a numerical simulation.