Runtime unknown unsafe scenarios identification for SOTIF of autonomous vehicles.

Safety is a critical concern for autonomous vehicles (AVs). Current testing approaches face challenges in simultaneously meeting the requirements of being valid, safe, and fast. To address these challenges, the silent testing approach that tests functions or systems in the background without interfering with driving is motivated.

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors.

Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc.

Functional decomposition-A contribution to overcome the parameter space explosion during validation of highly automated driving.

Particular testing by functional decomposition of the automated driving function can potentially contribute to reducing the effort of validating highly automated driving functions. In this study, the required size of test suites for scenario-based testing and the potential to reduce it by functional decomposition are quantified for the first time. The required size of test suites for scenario-based approval of a so-called Autobahn-Chauffeur (SAE Level 3) is analyzed for an exemplary set of scenarios.

Towards cooperative guidance and control of highly automated vehicles: H-Mode and Conduct-by-Wire.

This article provides a general ergonomic framework of cooperative guidance and control for vehicles with an emphasis on the cooperation between a human and a highly automated vehicle. In the twenty-first century, mobility and automation technologies are increasingly fused. In the sky, highly automated aircraft are flying with a high safety record.