Autonomous driving has been achieving great progress since last several years. However,
the autonomous vehicles always ignore the important traffic information on the road because …

Autonomous driving vehicles can reduce congestion and improve safety while increasing
traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety …

Knowledge transfer is a promising concept to achieve real-time decision-making for
autonomous vehicles. This paper constructs a transfer deep reinforcement learning (RL) …

The rapid growth in Autonomous Vehicle (AV) technology endeavors increased attention
towards road safety in recent days. Particularly, a higher number of road accidents occurs …

Compared with traditional rule-based algorithms, deep reinforcement learning methods in
autonomous driving are able to reduce the response time of vehicles to the driving …

Autonomous driving has been the trend. In this paper, a Deep Reinforcement Learning
(DRL) method is exploited to model the decision making and interaction between vehicles …

A novel framework is proposed for enhancing the driving safety and fuel economy of
autonomous vehicles (AVs) with the aid of vehicle-to-infrastructure (V2I) communication …

The establishment of future intelligent transport systems is dependable on the reliable and
seamless function of Connected and Autonomous Vehicles (CAV). Reinforcement learning …

Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic
transportation environments. In this paper, a reinforcement learning approach with value …

Reinforcement learning (RL) has made great progress in autonomous driving applications.
However, using one RL based driving policy for multi-scenarios autonomous driving is still …