Decision-making plays a crucial role in enabling automated driving and its performance is
highly dependent on accurate motion prediction. Most existing motion prediction methods …

From the driving strategy point of view, a major challenge for autonomous vehicles in urban
environment is to behave defensively to potential dangers, yet to not overreact to threats with …

The evolving autonomous driving technology has been attracting significant research efforts
in both academia and industry because of its promising potentials. Eliminating the human …

A Cognitive Hybrid Autonomous Motion Planner (CHAMP) is developed for autonomous
driving applications in challenging driving scenarios. The proposed hybrid planner unifies a …

Accurate trajectory tracking is unrealistic in real-world scenarios, however, which is
commonly assumed to facilitate motion planning algorithm design. In this paper, a safe and …

Autonomous vehicle technology is a promising development for improving the safety,
efficiency and environmental impact of on-road transportation systems. However, the task of …

Reinforcement learning (RL) has been widely used in the decision-making of autonomous
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …

Autonomous driving of multi-lane vehicle platoons have attracted significant attention in
recent years due to their potential to enhance the traffic-carrying capacity of the roads and …

Due to the development of advanced driver assistance systems (ADAS) and autonomous
vehicle (AV) technology, various path planning algorithms are being developed. However …

In this paper, we propose a progressive model predictive control scheme (PMPCS) by
considering the cooperative control of local planning and path tracking for intelligent …