… a deep reinforcement learning (DRL)-enabled decision-making policy is constructed for
autonomous vehicles to … aims to pass through the surrounding vehicles with an efficient and safe …

… In this paper, we propose a safe reinforcement learning framework combined with … safe
reinforcement learning approach in simulation scenario built in SUMO. Autonomous vehicle …

… driving safety is the ultimate concern for autonomous driving, … deep learning models and the
driving safety of autonomous … ’s impact on the driving safety of autonomous vehicles and the …

… keyword for each concept: safety, artificial intelligence and autonomous vehicle. In fact, a
search … representativeness, such as: machine learning, automated vehicle, self-driving and …

… of Machine learning (ML) components in many automated systems … safety of such systems
can be assessed by their compliance with safety standards [5], [6], such as the Road vehicles …

… Autonomous driving is presented as a highly disruptive feature for road means of transport,
capable of influencing aspects as fundamental as road safety and mobility itself. Over the last …

… in the search for the Autonomous Vehicles. It has been shown … The automated vehicle safety
is significant, and users are … combination of deep learning and Reinforcement Learning (RL…

… reaching SAE levels 4 and 5 widely adopt machine learning (ML) components to perform …
autonomous vehicle’s safety from two perspectives, the functional safety (FuSa) and the safety …

… In this paper, we developed an efficient and safety-enhanced offline RL framework with
application to autonomous driving in highway and parking traffic scenarios. To facilitate …

… challenging for the wide deployment of autonomous vehicles. Safety-critical systems in
general, require safe performance even during the reinforcement learning (RL) period. To …