Driver behavior models have been used as input to self-coaching, accident prevention
studies, and developing driver-assisting systems. In recent years, driver behavior …

Over the last decade, there has been rising interest in automated driving systems and
adaptive cruise control (ACC). Controllers based on reinforcement learning (RL) are …

Human-driven vehicles can amplify naturally occurring perturbations in traffic, leading to
congestion and consequently increased fuel consumption, higher collision risks, and …

Intelligent Transportation Systems (ITS) aim to enhance traffic management by improving
connectivity and data sharing among vehicles and with road infrastructure. In a Mixed …

Human-driven vehicles (HVs) exhibit complex and diverse behaviors. Accurately modeling
such behavior is crucial for validating Robot Vehicles (RVs) in simulation and realizing the …

Over the last decade, there has been increasing interest in autonomous driving systems.
Reinforcement Learning (RL) shows great promise for training autonomous driving …

Recently, intelligent control approaches through reinforcement learning have achieved
robust performance. The practical complication of reinforcement learning in approximating …

Recent research shows that the combination of reinforcement learning (RL) with traditional
control method can be an effective tool for designing near optimal feedback controller for …

Our transportation system is at the precipice of a paradigm shift with the significant and
growing efforts that have been dedicated to developing Connected and Automated …

Recently, intelligent control approaches through reinforcement learning have achieved
robust performance. The practical complication of reinforcement learning in approximating …