Roland Galbas

Exploiting learning and scenario-based specification languages for the verification and validation of highly automated driving

We propose a series of methods based on learning key structural properties from traffic data-basis and on statistical model checking, ultimately leading to the construction of a scenario catalogue capturing requirements for controlling criticality for highly autonomous vehicles. We sketch underlying mathematical foundations which allow to derive formal confidence levels that vehicles tested by such a scenario catalogue will maintain the required control of criticality in real traffic matching the probability distributions of key parameters of data recorded in the reference data base employed for this process.

Highly Automated Driving—Disruptive Elements and Consequences

The paper aims to give a compact overview of the changes towards the complete system “vehicle” imposed by the introduction of high-level-automation. At first, the changes are focused on three disruptive elements. Each of these elements is analyzed with respect to its requirements which are caused. Finally four main systemic changes are derived from the requirements. The idea of this breakdown is to propose a path aiming to structure the future developments in the area of highly automated driving (HAD). Highlighted are cross-over effects of requirements, the impact of framework conditions and derived consequences mainly from a technological point of view. Within the conclusions, some approaches are proposed in order to cope with these challenges—especially with focus on market deployment.