In the near future, autonomous vehicles (AVs) may cohabit with human drivers in mixed
traffic. This cohabitation raises serious challenges, both in terms of traffic flow and individual …

This paper proposes a deep reinforcement learning (DRL)‐based distributed longitudinal
control strategy for connected and automated vehicles (CAVs) under communication failure …

Connected and automated vehicles (CAVs) have emerged as a potential solution to the
future challenges of developing safe, efficient, and eco-friendly transportation systems …

This study proposes a vehicle platooning algorithm to minimize the disruption from a lane-
changing maneuver. Towards achieving this objective, while most studies emphasize the …

A promising way to improve efficiency in highway on-ramp regions is to control connected
and automated vehicles (CAVs) to pass the merging section sequentially. The primary …

There has been growing interest in deep reinforcement learning (DRL) algorithm design,
and reward design is one key component of DRL. Among the various techniques, formal …

Addressing safe and efficient interaction between connected and automated vehicles
(CAVs) and human-driven vehicles in a mixed-traffic environment has attracted considerable …

This paper proposes an improved decision-making method based on deep reinforcement
learning to address on-ramp merging challenges in highway autonomous driving. A novel …

Connected and automated vehicles (CAVs) provide the most intriguing opportunity for
enabling users to better monitor transportation network conditions and make better …

To improve the application range of decision‐making systems for connected automated
vehicles, this paper proposes a cooperative decision‐making approach for multiple driving …