This paper presents a safe learning-based eco-driving framework tailored for mixed traffic
flows, which aims to optimize energy efficiency while guaranteeing safety during real-system …

This study proposes autonomous eco-driving strategies for a traffic environment with limited
information available based on three popular Reinforcement Learning (RL) algorithms for …

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic
conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study …

Autonomous driving vehicles can reduce congestion and improve safety while increasing
traffic efficiency. To reflect the quality of driving more comprehensively, the driving safety …

This paper presents a novel approach for improving the safety of vehicles equipped with
Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical …

Autonomous driving is one of the most important AI applications and has attracted extensive
interest in recent years. A large number of studies have successfully applied reinforcement …

Connected and Automated Vehicles (CAVs), in particular those with multiple power sources,
have the potential to significantly reduce fuel consumption and travel time in real world …

In recent years, studies on practical application of automated driving have been conducted
extensively. Most of the research assumes the existing road infrastructure and aims to …

Reinforcement learning is recently widely used in the field of Autonomous Vehicles as it
became a powerful algorithm in the artificial intelligence field. In this paper, Reinforcement …

Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and
significantly aiding in building autonomous systems with a higher level comprehension of …