Object detection algorithms are essential components for perceiving the environment in
safety-critical systems like automated driving. However, current state-of-the-art algorithms …

Perception is a safety-critical function of autonomous vehicles and machine learning (ML)
plays a key role in its implementation. This position paper identifies (1) perceptual …

Motion prediction is the fundamental input for decision-making in autonomous vehicles. The
current motion prediction solutions are designed with a strong reliance on black box …

Uncertain environment on multi-lane highway, eg, the stochastic lane-change maneuver of
surrounding vehicles, is a big challenge for achieving safe automated highway driving. To …

Reinforcement learning (RL) can be used to create a decision-making agent for autonomous
driving. However, previous approaches provide black-box solutions, which do not offer …

A rise in popularity of Deep Neural Networks (DNNs), attributed to more powerful GPUs and
widely available datasets, has seen them being increasingly used within safety-critical …

Self-evaluation and monitoring are critical components in autonomous driving applications,
especially for safety purposes, and yet there is no systematic framework to estimate the …

Autonomous driving is considered one of the revolutionary technologies shaping humanity's
future mobility and quality of life. However, safety remains a critical hurdle in the way of …

Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …

Current automotive safety standards are cautious when it comes to utilizing deep neural
networks in safety-critical scenarios due to concerns regarding robustness to noise, domain …