Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

The rising popularity of self-driving cars has led to the emergence of a new research field in
recent years: Autonomous racing. Researchers are develo** software and hardware for …

Neural networks predictions are unreliable when the input sample is out of the training
distribution or corrupted by noise. Being able to detect such failures automatically is …

Deep neural network controllers for autonomous driving have recently benefited from
significant performance improvements, and have begun deployment in the real world. Prior …

While classical autonomous navigation systems can typically move robots from one point to
another safely and in a collision-free manner, these systems may fail or produce suboptimal …

A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal
behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on …

Abstract Graph Neural Networks (GNN) provide a powerful framework that elegantly
integrates Graph theory with Machine learning for modeling and analysis of networked data …

In the domain of autonomous driving, the offline Reinforcement Learning (RL) approaches
exhibit notable efficacy in addressing sequential decision-making problems from offline …

Nighttime conditions pose a significant challenge to color constancy due to the diversity of
lighting conditions and the presence of substantial low-light noise. Existing color constancy …

We present a method for learning to drive on smooth terrain while simultaneously avoiding
collisions in challenging off-road and unstructured outdoor environments using only visual …