Many potential applications of artificial intelligence involve making real-time decisions in
physical systems while interacting with humans. Automobile racing represents an extreme …

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 …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Professional race-car drivers can execute extreme overtaking maneuvers. However, existing
algorithms for autonomous overtaking either rely on simplified assumptions about the …

Motorsport has always been an enabler for technological advancement, and the same
applies to the autonomous driving industry. The team TUM Autonomous Motorsports will …

Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on
Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a …

World models learn behaviors in a latent imagination space to enhance the sample-
efficiency of deep reinforcement learning (RL) algorithms. While learning world models for …

Data-driven simulators promise high data-efficiency for driving policy learning. When used
for modelling interactions, this data-efficiency becomes a bottleneck: small underlying …

Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to
humans, automated vehicles are supposed to perform anomaly detection. In this work, we …

We present an approach for safe trajectory planning, where a strategic task related to
autonomous racing is learned sample efficiently within a simulation environment. A high …