Smoothing Mixed Traffic with Robust Data-driven Predictive Control for Connected and Autonomous Vehicles

TL;DR

This paper introduces a robust data-driven predictive control method for connected and autonomous vehicles that enhances safety and traffic smoothing in mixed traffic scenarios by accounting for potential velocity errors.

Contribution

It extends the DeeP-LCC method with a robust formulation that considers velocity error uncertainties, improving safety and efficiency in traffic control.

Findings

Better traffic flow smoothing in simulations

Enhanced safety performance under velocity errors

Requires less offline data for effective control

Abstract

The recently developed DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) method has shown promising performance for data-driven predictive control of Connected and Autonomous Vehicles (CAVs) in mixed traffic. However, its simplistic zero assumption of the future velocity errors for the head vehicle may pose safety concerns and limit its performance of smoothing traffic flow. In this paper, we propose a robust DeeP-LCC method to control CAVs in mixed traffic with enhanced safety performance. In particular, we first present a robust formulation that enforces a safety constraint for a range of potential velocity error trajectories, and then estimate all potential velocity errors based on the past data from the head vehicle. We also provide efficient computational approaches to solve the robust optimization for online predictive control. Nonlinear traffic simulations show that our robust DeeP-LCC can provide better traffic efficiency and stronger safety performance while requiring less offline data.