Considering that human-driven vehicles and autonomous vehicles (AVs) will coexist on
roads in the future for a long time, how to merge AVs into human drivers' traffic ecology and …

Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence
(AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the …

With the aims of safe, smart and sustainable future mobility, a personalized approach of
trajectory planning and control based on user preferences is developed for lane-change of …

With the development of intelligent vehicle technology, many studies have been focused on
developing human-like trajectory planning methods for automated driving systems. Although …

In the plan and development of Intelligent Transportation Systems (ITS), understanding
drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive …

Motion planning for autonomous vehicles becomes more challenging when both driver
comfort and collision risk are considered. To overcome this challenge, a human-like motion …

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …

Inspired by human visual attention, we propose a novel inverse reinforcement learning
formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for …

As the main component of an autonomous driving system, the motion planner plays an
essential role for safe and efficient driving. However, traditional motion planners cannot …

Driver model is the most basic and important model for moving direction control of
autonomous vehicles and it has been extensively studied from the perspective of precision …