Autonomous driving (AD) holds the potential to revolutionize transportation efficiency, but its
success hinges on robust behavior planning (BP) mechanisms. Reinforcement learning (RL) …

In this paper, a novel approach is presented for modeling the interaction dynamics between
an ego car and a bicycle in a traffic scenario using a hybrid reinforcement learning …

Deep reinforcement learning (DRL) has shown remarkable success in complex autonomous
driving scenarios. However, DRL models inevitably bring high memory consumption and …

Abstract Design/methodology/approach We propose a novel path inference approach based
on Voronoi graph for unmanned maritime vehicles (UMV). We model the two-dimensional …

Grid planning is a long-term process with uncertainties at all stages, which requires a
versatile design approach to address potential risks. From the perspectives of system …

The aim of this work, which is an extension of previous research, is a comparative analysis
of the results of the dynamic optimization of safe multi-object control, with different …

This research investigates optimization-based schemes aimed at achieving effective
collision avoidance in autonomous vehicles. The study introduces three explicit formulations …

In this article, an evolution-guided adaptive dynamic programming (EGADP) algorithm is
developed to address the optimal regulation problems for the nonlinear systems. In the …

In this article, a real-time online off-policy reinforcement learning (RL) method is developed
for the optimal control problem of unknown continuous-time nonlinear systems. First, by …

In this article, an evolution-guided value iteration (EGVI) algorithm is established to address
optimal tracking problems for nonlinear nonaffine systems. Conventional adaptive dynamic …