The objective of this dissertation is to develop deep reinforcement learning for multiple
autonomous vehicles under mixed traffic conditions in non-signalized junctions. To achieve …

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 …

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 …

Automated driving systems have become a potential approach to mitigating collisions,
emissions, and human errors in mixed-traffic environments. This study proposes the use of a …

To reduce the impact of congestion, it is necessary to improve our overall understanding of
the influence of the autonomous vehicle. Recently, deep reinforcement learning has become …

Decision-making for automatic vehicles at unsignalized intersections with dense traffic is
one of the most challenging tasks. Due to the complex structure and frequent traffic …

Decision making at unsignalized intersections is a critical challenge for autonomous
vehicles. Navigating through urban intersections requires determining the intentions of other …

Coordinating autonomous vehicles (AVs) at signal-free intersections has emerged as a
critical area of research in intelligent transportation. This paper presents a novel vehicle …

The number of connected and automated vehicles in urban areas will gradually increase in
the near future. As a consequence, mixed traffic made of both regular human-driven and …

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