Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach

TL;DR

This paper introduces a distributionally robust model predictive control method for highway lane-change scenarios, which adapts to uncertain driver behaviors by updating probabilistic models based on real-time observations.

Contribution

It develops a novel learning-based robust control framework that accounts for uncertain lane change probabilities using Markov jump systems and ambiguity sets, improving safety and adaptability.

Findings

Controller reduces conservativeness with more observations

Method enhances safety under uncertain driver behaviors

Numerical case study demonstrates improved performance

Abstract

We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users. The dynamics of road users are modelled using Markov jump systems, in which the switching variable describes the desired lane of the vehicle under consideration and the continuous state describes the pose and velocity of the vehicles. We assume the switching probabilities of the underlying Markov chain to be unknown. As the vehicle is observed and thus, samples from the Markov chain are drawn, the transition probabilities are estimated along with an ambiguity set which accounts for misestimations of these probabilities. Correspondingly, a distributionally robust optimal control problem is formulated over a scenario tree, and solved in receding horizon. As a result, a motion planning procedure is obtained which through observation of the target vehicle gradually becomes less conservative while avoiding overconfidence in estimates obtained from small sample sizes. We present an extensive numerical case study, comparing the effects of several different design aspects on the controller performance and safety.