Ensuring that autonomous vehicles do not cause accidents remains a challenge. We
present a formal verification technique for guaranteeing legal safety in arbitrary urban traffic …

Safe motion planning for autonomous vehicles is a challenging task, since the exact future
motion of other traffic participant is usually unknown. In this article, we present a verification …

Formally verified methods for motion planning are required in order to guarantee safety for
autonomous vehicles. In particular, we consider trajectory generation by considering the …

Provably safe motion planning for automated road vehicles must ensure that planned
motions do not result in a collision with other traffic participants. This is a major challenge in …

Validating the safety of self-driving vehicles requires an enormous amount of testing. By
applying formal verification methods, we can prove the correctness of the vehicles' behavior …

The safety validation of motion planning algorithms for automated vehicles requires a large
amount of data for virtual testing. Currently, this data is often collected through real test …

The goal of autonomous vehicles is to navigate public roads safely and comfortably. To
enforce safety, traditional planning approaches rely on handcrafted rules to generate …

In this paper we present the first safe system for full control of self-driving vehicles trained
from human demonstrations and deployed in challenging, real-world, urban environments …

Ensuring the safety of self-driving vehicles is a challenging task, especially if other traffic
participants severely deviate from the predicted behavior. One solution is to ensure that the …

One significant barrier in introducing autonomous driving is the liability issue of a collision;
eg when two autonomous vehicles collide, it is unclear which vehicle should be held …