Autonomous vehicle navigation in an unknown dynamic environment is crucial for both
supervised-and Reinforcement Learning-based autonomous maneuvering. The cooperative …

High-level driving behavior decision-making is an open-challenging problem for connected
vehicle technology, especially in heterogeneous traffic scenarios. In this paper, a deep …

Bottleneck areas are prone to severe traffic congestion due to the sudden drop in capacity.
To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep …

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 …

In this paper, we generated intelligent self-driving policies that minimize the injury severity in
unexpected traffic signal violation scenarios at an intersection using the deep reinforcement …

Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …

The essential of developing an advanced driving assistance system is to learn human-like
decisions to enhance driving safety. When controlling a vehicle, joining roundabouts …

Autonomous vehicles can reduce labor power during cargo transportation, and then improve
transportation efficiency, for example, the automated guided vehicle (AGV) in the warehouse …

Lane-changing (LC) is a critical task for autonomous driving, especially in complex dynamic
environments. Numerous automatic LC algorithms have been proposed. This topic …