In this work, path tracking control of autonomous vehicles (AVs) are studied under the
framework of adaptive iterative learning control (AILC). In order to facilitate the controller …

Vehicle trajectory planning is a key component for an autonomous driving system. A
practical system not only requires the component to compute a feasible trajectory, but also a …

Interaction-aware trajectory planning is crucial for closing the gap between autonomous
racing cars and human racing drivers. Prior work has applied game theory as it provides …

Advanced driver assistance systems have gained popularity as a safe technology that helps
people avoid traffic accidents. To improve system reliability, a lot of research on driving …

Motion planning under uncertainty is of significant importance for safety-critical systems such
as autonomous vehicles. Such systems have to satisfy necessary constraints (eg, collision …

By optimizing the predicted performance over a receding horizon, model predictive control
(MPC) provides the ability to enforce state and control constraints. The present paper …

Trajectory planning for autonomous race cars poses special challenges due to the highly
interactive and competitive environment. Prior work has applied game theory as it provides …

This work proposes a novel framework for motion planning using trajectory optimization for
autonomous driving. First, a two-phase behavioral policy maker (BPM) is proposed as a high …

Motion planning is essential for an autonomous vehicle to perform safe and human-like
driving behaviors, especially in highly dynamic scenarios such as dense urban and highway …

Abstract Machine learning-based techniques have shown great promises in perception,
prediction, planning, and general decision-making for improving task performance of …