Deep reinforcement learning (RL) has been widely applied to motion planning problems of
autonomous vehicles in urban traffic. However, traditional deep RL algorithms cannot …

This paper presents a systematic decision-making and lateral control framework to realize
cooperative obstacle avoidance (OA) of the guided vehicle platoons safely and integrally in …

Intersections are quite challenging among various driving scenes wherein the interaction of
signal lights and distinct traffic actors poses great difficulty to learn a wise and robust driving …

The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—
presents significant challenges in ensuring the safety of autonomous vehicles using deep …

Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …

The multi-lane roundabout poses significant challenges for autonomous driving due to its
complex road structure and traffic conditions. To address these challenges, this paper …

For the last two decades, autonomous vehicles have been proposed and developed to
extend the operational design domain from the motorway to urban environments. However …

Decision-making technology of autonomous vehicle is promoted by the development of
reinforcement learning, and intelligent decision-making technology has become a key issue …

The comprehensiveness of vehicle-to-everything (V2X) recognition enriches and holistically
shapes the global Birds-Eye-View (BEV) perception, incorporating rich semantics and …

Controlling a real-world system with state constraints has drawn increasing attention due to
practical needs, such as operating limits and safety guarantees. Equipping RL/ADP with the …