Potential fields have been integrated with local path-planning algorithms for autonomous
vehicles (AVs) to tackle challenging scenarios with dense and dynamic obstacles. Most …

Machine learning techniques have been shown to outperform many rule-based systems for
the decision-making of autonomous vehicles. However, applying machine learning is …

Abstract The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in
motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which …

During automated driving in urban areas, decisions must be made while recognizing the
surrounding environment using sensors such as camera, Light Detection and Ranging …

This paper presents a novel integrated approach to deal with the decision making and
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …

As a typical example of cyber-physical systems, intelligent vehicles are receiving increasing
attention, and the obstacle avoidance problem for such vehicles has become a hot topic of …

This paper presents a hierarchical trajectory planning based on the integration of a sampling
and an optimization method for urban autonomous driving. To manage a complex driving …

Rigorous and comprehensive testing plays a key role in training self-driving cars to handle a
variety of situations that they are expected to see on public roads. The physical testing on …

Every revolution promises a spirit of optimism and significant changes. Connected and
automated vehicles (CAVs) are coming to our roadways. CAVs have a crucial role to play in …

Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …