Decision making is an essential component of autonomous vehicle technology and received
significant attention from academic and industry organizations. One of the promising …

In a multi-agent system (MAS), a social learning scheme allows independent agents to learn
through interactions with agents randomly selected from a pool. Such a scheme is important …

Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …

Reinforcement learning (RL) has gained significant interest for its potential to improve
decision and control in autonomous driving. However, current approaches have yet to …

Connected and autonomous vehicles are expected to improve mobility and transportation,
as well as to provide energy efficiency benefits. The integration of safety and energy …

Traffic crisis often happen because of traffic burden by the large number automobiles on the
path. Increasing transportation move and decreasing the average waiting time of each …

Predictive cruise control concerns designing controllers for autonomous vehicles using the
broadcasted information from the traffic lights such that the idle time around the intersection …

End to end learning systems are becoming increasingly common in autonomous driving
research, from perception, to planning and control. In particular, distributed reinforcement …

Eco-driving represents a promising avenue for mitigating energy consumption in road
transportation. To enhance the applicability of learning-based eco-driving strategies, this …

Embedded artificial intelligence solutions are promising controllers for future sustainable
and automated road vehicles. This study presents a deep learning-based approach …