Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …

Autonomous driving has achieved significant milestones in research and development over
the last decade. There is increasing interest in the field as the deployment of self-operating …

Deployment of Reinforcement Learning (RL) algorithms for robotics applications in the real
world requires ensuring the safety of the robot and its environment. Safe Robot RL (SRRL) is …

In recent years, the advancement of deep-learning technologies has greatly promoted the
research progress of autonomous driving. However, deep neural network is like a black box …

Computer vision applications in intelligent transportation systems (ITS) and autonomous
driving (AD) have gravitated towards deep neural network architectures in recent years …

Artificial intelligence models encounter significant challenges due to their black-box nature,
particularly in safety-critical domains such as healthcare, finance, and autonomous vehicles …

In Autonomous Driving (AD) transparency and safety are paramount, as mistakes are costly.
However, neural networks used in AD systems are generally considered black boxes. As a …

Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-
making process and provide opportunities in various aspects of explaining AI models. This …

Artificial Intelligence (AI) is the machine simulation of human intelligence with the capability
to perform various tasks. It has brought rapid changes in modern world applications and has …

Autonomous vehicles are expected as a way to reduce traffic-related fatalities. Nighttime
traffic causes only about one-third of fatalities but more than half are pedestrian or cyclist …