Recently, the concept of autonomous driving became prevalent in the domain of intelligent
transportation due to the promises of increased safety, traffic efficiency, fuel economy and …

The complex traffic and road environment pose considerable challenges to the accuracy,
timeliness, and adaptive ability of connected and autonomous vehicles (CAVs) in making …

Decision-making for autonomous vehicles is critical to achieving safe and efficient
autonomous driving. In recent years, deep reinforcement learning (DRL) techniques have …

Currently, the complex traffic environment challenges the fast and accurate response of a
connected autonomous vehicle (CAV). More importantly, it is difficult for different CAVs to …

Driving through complex urban environments is a challenging task for autonomous vehicles
(AVs), as they must safely reach their mission goal, and react properly to traffic participants …

Automated navigation technology has established itself as an integral facet of intelligent
transportation and smart city systems. Several international technological organizations …

The openness of the intelligent vehicle network makes it easy for selfish or untrustworthy
vehicles to maliciously occupy limited resources in the mobile edge network or spread …

Learning-based approaches, such as reinforcement learning (RL) and imitation learning
(IL), have indicated superiority over rule-based approaches in complex urban autonomous …

There has been recent and growing interest in the development and deployment of
autonomous vehicles, encouraged by the empirical successes of powerful artificial …

Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …