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

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

Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that
focuses on the development of autonomous vehicles (AVs) that are capable of interacting …

For decades, motorsport has been an incubator for innovations in the automotive sector and
brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such …

Abstract In most classical Autonomous Vehicle (AV) stacks, the prediction and planning
layers are separated, limiting the planner to react to predictions that are not informed by 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 …

Cooperative multi-robot tasks can benefit from heterogeneity in the robots' physical and
behavioral traits. In spite of this, traditional Multi-Agent Reinforcement Learning (MARL) …

Dynamic games are an effective paradigm for dealing with the control of multiple interacting
actors. This paper introduces augmented Lagrangian GAME-theoretic solver (ALGAMES), a …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

The computer vision systems driving autonomous vehicles are judged by their ability to
detect objects and obstacles in the vicinity of the vehicle in diverse environments. Enhancing …