Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical
practice during the past two decades. Many believe that the next big step in the …

Realistic and diverse simulation scenarios with reactive and feasible agent behaviors can
be used for validation and verification of self-driving system performance without relying on …

Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …

Recent innovation in highly automated driving in industrial and scientific domains has
created a growing demand for logical description of statistically meaningful real-world …

Despite promising progress in reinforcement learning (RL), developing algorithms for
autonomous driving (AD) remains challenging: one of the critical issues being the absence …

Autonomous driving technologies often use state-of-the-art artificial intelligence algorithms
to understand the relationship between the vehicle and the external environment, to predict …

We propose that vehicles driven by distracted drivers take a higher priority in multiple access
for vehicle-to-everything communications (V2X). As more vehicles communicate, it is one's …

Before implementing Autonomous Vehicles (AVs) in real-world settings, it is imperative to
conduct thorough safety testing. Virtual simulation testing, known for its high fidelity, cost …

The self driving challenge in 2021 is this century's technological equivalent of the space
race, and is now entering the second major decade of development. Solving the technology …

With the ever-increasing advancements in the technology of driver assistant systems, there
is a need for a comprehensive way to identify traffic conflicts to avoid collisions. Although …