Reinforcement learning (RL) can free automated vehicles (AVs) from the car-following
constraints and provide more possible explorations for mixed behavior. This study uses …

The emergence and application of connected and automated vehicles (CAVs) have played
a positive role in improving the efficiency of urban transportation and achieving sustainable …

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and
human-driven vehicles (HVs) at a signalized intersection has become a concern of …

Advanced deep reinforcement learning shows promise as an approach to addressing
continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a …

Development of new approaches to adaptive traffic signal control has received significant
attention; an example is the reinforcement learning (RL), where training and implementation …

Trajectory optimization for connected automated vehicles (CAVs) is an effective method to
improve the overall performance of urban traffic. At the same time, the emergence of deep …

Human driven vehicles (HDVs) with selfish objectives cause low traffic efficiency in an un-
signalized intersection. On the other hand, autonomous vehicles can overcome this …

Most studies developing car-following controllers for AVs in mixed traffic primarily focus on
maximizing the utility of the AVs. However, the utility of the entire mixed traffic flow is largely …

This paper proposes a hierarchical framework integrating deep reinforcement learning
(DRL) and rule-based methods for multi-lane autonomous driving. We define an …

The development of intelligent driving technologies is expected to have the potential in
energy economics. Some reported studies mainly focused on the economical driving …