A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay

TL;DR

This paper presents a stochastic model predictive control method for coordinating human-driven vehicles at intersections, accounting for uncertain driver reaction delays to ensure safety without traffic signals.

Contribution

It introduces a distributed scenario-based stochastic MPC that handles driver reaction time uncertainties, providing probabilistic safety guarantees in intersection crossing.

Findings

Successfully avoids collisions despite driver delay uncertainties

Outperforms non-stochastic controllers in safety guarantees

Demonstrates effectiveness through simulation results

Abstract

We investigate the problem of coordinating human-driven vehicles in road intersections without any traffic lights or signs by issuing speed advices. The vehicles in the intersection are assumed to move along an a priori known path and to be connected via vehicle-to-vehicle communication. The challenge arises with the uncertain driver reaction to a speed advice, especially in terms of the driver reaction time delay, as it might lead to unstable system dynamics. For this control problem, a distributed stochastic model predictive control concept is designed which accounts for driver uncertainties. By optimizing over scenarios, which are sequences of independent and identically distributed samples of the uncertainty over the prediction horizon, we can give probabilistic guarantees on constraint satisfaction. Simulation results demonstrate that the scenario-based approach is able to avoid collisions in spite of uncertainty while the non-stochastic baseline controller is not.