Chance–constrained optimization has been studied in the context of autonomous vehicles.
The work in [4] addresses the tactical planning problem for autonomous vehicles in …

Driving requires forecasts. Forecasted movements of objects in the driving scene are
uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope …

In this paper a robust control framework is proposed for the lane-keeping and obstacle
avoidance of semi-autonomous ground vehicles. A robust Model Predictive Control …

Automated driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

Human-driven and autonomously driven cars of today act often reactively to the decisions of
the cars they follow, which could lead to uncomfortable, inefficient, and sometimes unsafe …

Autonomous vehicles need to be able to plan trajectories to a specified goal that avoid
obstacles, and are robust to the inherent uncertainty in the problem. This uncertainty arises …

Autonomous driving requires decision making in dynamic and uncertain environments. The
uncertainty from the prediction originates from the noisy sensor data and from the fact that …

A robust control design is proposed for the lane-keeping and obstacle avoidance of
semiautonomous ground vehicles. A robust Model Predictive Controller (MPC) is used in …

Human drivers are endowed with an inborn ability to put themselves in the position of other
drivers and reason about their behavior and intended actions. State-of-the-art driving …

This paper considers the problem of decision making and control for autonomous urban
vehicles operating among other non-cooperating, possibly human controlled, vehicles. The …