Decision-making on numerous aspects of our daily lives is being outsourced to machine-
learning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in …

Abstract Nowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all
fields of technology, and Autonomous Vehicles (AV) research is one more of them. This …

End-to-end autonomous driving provides a feasible way to automatically maximize overall
driving system performance by directly map** the raw pixels from a front-facing camera to …

Recent advances in Reinforcement Learning (RL) combined with Deep Learning (DL) have
demonstrated impressive performance in complex tasks, including autonomous driving. The …

Ensuring the safety of pedestrians is essential and challenging when autonomous vehicles
are involved. Classical pedestrian avoidance strategies cannot handle uncertainty, and …

The use of neural networks and reinforcement learning has become increasingly popular in
autonomous vehicle control. However, the opaqueness of the resulting control policies …

Since the field of autonomous vehicles is develo** quickly, it is becoming increasingly
crucial for them to safely and effectively navigate their surroundings to avoid collisions. The …

An accurate model of human drivers is essential to validate the performance of autonomous
vehicles in multiagent and interactive scenarios. Previous works on human driver modeling …

Model-based offline reinforcement learning (RL) algorithms have emerged as a promising
paradigm for offline RL. These algorithms usually learn a dynamics model from a static …

The scenario-based testing of operational vehicle safety presents a set of principal other
vehicle (POV) trajectories that seek to force the subject vehicle (SV) into a certain safety …