Safely Optimizing Highway Traffic with Robust Model Predictive Control-based Cooperative Adaptive Cruise Control

TL;DR

This paper introduces a robust model predictive control approach for cooperative adaptive cruise control that ensures safety distances are maintained despite uncertainties and disturbances, enhancing highway traffic safety and efficiency.

Contribution

It develops an $l ext{-}ty$-norm robust MPC that guarantees safety distances under uncertainties, a novel formulation for minimum clearance, and demonstrates improved performance over nominal controllers.

Findings

The proposed RMPC maintains safety distances under disturbances.

Simulation shows improved safety and efficiency over nominal control.

The approach effectively handles uncertainties in vehicle behavior.

Abstract

Road traffic crashes have been the leading cause of death among young people. Most of these accidents occur when the driver becomes distracted due to fatigue or external factors. Vehicle platooning systems such as Cooperative Adaptive Cruise Control (CACC) are one of the results of the effort devoted to the development of technologies for decreasing the number of road crashes and fatalities. Previous studies have suggested such systems improve up to 273\% highway traffic throughput and fuel consumption in more than 15\% if the clearance between vehicles in this class of roads can be reduced to 2 meters. This paper proposes an approach that guarantees a minimum safety distance between vehicles taking into account the overall system delays and braking capacity of each vehicle. A $l\infty$-norm Robust Model Predictive Controller (RMPC) is developed to guarantee the minimum safety distance is not violated due to uncertainties on the lead vehicle behavior. A formulation for a lower bound clearance of vehicles inside a platoon is also proposed. Simulation results show the performance of the proposed approach compared to a nominal controller when the system is subject to both modeled and unmodeled disturbances.