Socially aware robot navigation is gaining popularity with the increase in delivery and
assistive robots. The research is further fueled by a need for socially aware navigation skills …

In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential
rate, particularly due to improvements in artificial intelligence, which have had a significant …

Although deep reinforcement learning (DRL) methods are promising for making behavioral
decisions in autonomous vehicles (AVs), their low training efficiency and difficulty to adapt to …

The advancement in sensor technologies, mobile network technologies, and artificial
intelligence has pushed the boundaries of different verticals, eg, eHealth and autonomous …

Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of
safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where …

Driving requires the ability to handle unpredictable situations. Since it is not always possible
to predict an impending danger, a good driver should preventively assess whether a …

This paper proposes a systematic driving framework where the decision making module of
reinforcement learning (RL) is integrated with rapidly-exploring random tree (RRT) as …

Incomplete knowledge of the environment leads an agent to make decisions under
uncertainty. One of the major dilemmas in Reinforcement Learning (RL) where an …

This paper presents a white-box intention-aware decision-making for the handling of
interactions between a pedestrian and an automated vehicle (AV) in an unsignalized street …

Urban autonomous driving in the presence of pedestrians as vulnerable road users is still a
challenging and less examined research problem. This work formulates navigation in urban …