Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …

Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …

Youla-Kucera (YK) parametrization was formulated decades ago for obtaining the set of
controllers stabilizing a linear plant. This fundamental result of control theory has been used …

The design of high-level decision-making systems is a topical problem in the field of
autonomous driving. In this paper, we combine traditional rule-based strategies and …

Using deep reinforcement learning, we successfully train a set of two autonomous vehicles
to lead a fleet of vehicles onto a round-about and then transfer this policy from simulation to …

The fast development of technology and social media have both brought about substantial
changes to the ways in which humans communicate. Because the effectiveness of social …

Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have
demonstrated impressive performance in complex tasks, including autonomous driving. The …

This paper proposes an adversarial reinforcement learning (RL)-based traffic control
strategy to improve the traffic efficiency of an integrated network with expressway and …

Among a number of problems in the behavior planning of an unmanned vehicle the central
one is movement in difficult areas. In particular, such areas are intersections at which direct …

Reinforcement learning (RL) plays an essential role in the field of artificial intelligence but
suffers from data inefficiency and model-shift issues. One possible solution to deal with such …