With the development of communication and networking technologies, the Internet of
Vehicles (IoV) has become the foundation of smart transportation. The development of …

The learning from intervention (LfI) approach has been proven effective in improving the
performance of RL algorithms; nevertheless, existing methodologies in this domain tend to …

Autonomous vehicles are expected to achieve self-evolution in the real-world environment
to gradually cover more complex and changing scenarios. Reinforcement learning focuses …

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

Lane changes require dynamic decision-making and rapid behavior planning, which are
challenging for traffic modeling. We propose a two-dimensional following lane-changing …

This article develops an autonomous cooperative interception method with angle
constraints, allowing a swarm of unmanned aerial vehicles (UAVs) to simultaneously …

Driver models are crucial for the safety assessment of autonomous vehicles (AVs) because
of their role as reference models. Specifically, an AV is expected to achieve at least the …

Mandatory lane-change scenarios are often challenging for autonomous vehicles in
complex environments. In this paper, a human-knowledge-enhanced reinforcement learning …

Connectionist artificial intelligence (AI) can power many critical tasks for connected and
autonomous vehicles (CAVs). However, connectionist AI lacks interpretability and usually …

The development of connected and automated vehicles (CAVs) presents a great opportunity
to extend the current range of vehicle vision, by gathering information outside of its sensors …