This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Robot navigation in crowded public spaces is a complex task that requires addressing a
variety of engineering and human factors challenges. These challenges have motivated a …

Driving behavior modeling is of great importance for designing safe, smart, and
personalized autonomous driving systems. In this paper, an internal reward function-based …

Scenario understanding and motion prediction are essential components for completely
replacing human drivers and for enabling highly and fully automated driving (SAE-Level …

Driver behavior models have been used as input to self-coaching, accident prevention
studies, and developing driver-assisting systems. In recent years, driver behavior …

Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …

Due to the growing interest in automated driving, a deep understanding on the
characteristics of human driving behavior is critical for human-like autonomous vehicles …

Autonomous vehicles must reason about spatial occlusions in urban environments to ensure
safety without being overly cautious. Prior work explored occlusion inference from observed …

Autonomous vehicles need to model the behavior of surrounding human driven vehicles to
be safe and efficient traffic participants. Existing approaches to modeling human driving …

Driver behavior modeling is an important task for predicting or simulating the evolution of
traffic situations. We investigate the use of Adversarial Inverse Reinforcement Learning …