… dynamic adaptation of the inter-vehicle gap based on deep reinforcement learning (RL). A
… ing safety model parameters and inter-vehicle gap based on macroscopic and microscopic …

… Abstract—The operational space of an autonomous vehicle (AV) can be diverse and vary …
a reinforcement learning (RL) based method, where the ego car, ie, an autonomous vehicle, …

… in mixed-autonomy traffic. In this study, we present a deep reinforcement-learning-based
model that considers the effectiveness of leading autonomous vehicles in mixed-autonomy …

… Reinforcement Learning (DRL) in uncertain traffic flows to resolve chain collision avoidance
difficulties among multiple autonomous vehicles, … , we define our autonomous vehicle driving …

… overview into the reinforcement learning algorithms and traffic … control strategies for
autonomous vehicles, traffic lights, etc. … and on training autonomous vehicles to maximize system-…

… a growing trend of applying advanced reinforcement learning (RL) … tracking problem of
autonomous surface vehicles, where a RL … in IEEE Transactions on Intelligent Vehicles. This is the …

… We focus on the partially observed setting of observing only the velocity of the autonomous
vehicle, the velocity of its preceding vehicle, and its relative position to the preceding …

… our deep reinforcement learning agents. Secondly, we investigate the leading autonomous
vehicle experiment in the urban network with different autonomous vehicle penetration rates. …

… information to vehicles through Vehicle-to-… Reinforcement Learning based approach that
integrates the data collected through sensing and connectivity capabilities from other vehicles …

… So we combine this framework with reinforcement learning in this paper to improve its
ability to deal with the highly dynamic environment. Considering the complexity of high-…