The rapid development of intelligent and connected technologies is conducive to the
efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy …

Thanks to their road safety potential, automated vehicles are rapidly becoming a reality. In
the last decade, automated driving has been the focus of intensive automotive engineering …

Motion prediction is the fundamental input for decision-making in autonomous vehicles. The
current motion prediction solutions are designed with a strong reliance on black box …

Advanced algorithms can promote the development of energy management strategies
(EMSs) as a key technology in hybrid electric vehicles (HEVs). Reinforcement learning (RL) …

This paper proposes a novel energy distribution optimization method of hybrid energy
storage system (HESS) and its improved semi-active topology for electric vehicles (EVs) to …

Reinforcement learning (RL) methods are commonly regarded as effective solutions for
designing intelligent driving policies. Nonetheless, even if the RL policy is converged after …

In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …

Planning and decision-making technology at intersections is a comprehensive research
problem in intelligent transportation systems due to the uncertainties caused by a variety of …

Practical vision-based technology is essential for the autonomous driving of intelligent
hybrid electric vehicles. In this article, a hierarchical control structure is proposed, which …

Autonomous vehicles require safe motion planning in uncertain environments, which are
largely caused by surrounding vehicles. In this paper, a driving environment uncertainty …