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 …

Deployment of autonomous vehicles on public roads promises increased efficiency and
safety. It requires understanding the intent of human drivers and adapting to their driving …

The actions of an autonomous vehicle on the road affect and are affected by those of other
drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual …

The deployment and evaluation of learning algorithms on autonomous vehicles (AV) is
expensive, slow, and potentially unsafe. This paper details the F1TENTH autonomous …

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 …

In this article, we propose an online 3-D planning algorithm for a drone to race competitively
against a single adversary drone. The algorithm computes an approximation of the Nash …

In this article, we propose a nonlinear receding horizon game-theoretic planner for
autonomous cars in competitive scenarios with other cars. The online planner is specifically …

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 …