Tobias Dirndorfer

A novel approach for intelligent pre-crash threat assessment systems

Compared to the state-of-the-art on integrated safety systems, earlier activated safety systems can further reduce the risk of suffering a major injury. Activation of such systems prior to a collision can be realized by analysing measurements of exteroceptive sensors (pre-crash data). An algorithm for estimating collisions in real-time using fused measurements of a video camera, a laser range finder (LRF), and ego vehicle motion sensors is presented. The threat posed by the actual driving situation is assessed by calculating a certain risk value, which is determined by combining the collision probability and crash severity estimations in a comprehensive way. A scale model vehicle is introduced to capture characteristics of the proposed system experimentally. First test runs show that the object width measurement is very accurate (absolute error of 5%) and the maximum time to collision (TTC) estimation error is around 17% about 300ms before the impact. Comparing different obstacles and impact scenarios (e.g. small overlap vs. full frontal collision), the calculated risk is a promising new measure to early discriminate crash types.

Context-aware system for pre-triggering irreversible vehicle safety actuators

New vehicle safety systems have led to a steady improvement of road safety and a reduction in the risk of suffering a major injury in vehicle accidents. A huge leap forward in the development of new vehicle safety systems are actuators that have to be activated irreversibly shortly before a collision in order to mitigate accident consequences. The triggering decision has to be based on measurements of exteroceptive sensors currently used in driver assistance systems. This paper focuses on developing a novel context-aware system designed to detect potential collisions and to trigger safety actuators even before an accident occurs. In this context, the analysis examines the information that can be collected from exteroceptive sensors (pre-crash data) to predict a certain collision and its severity to decide whether a triggering is entitled or not. A five-layer context-aware architecture is presented, that is able to collect contextual information about the vehicle environment and the actual driving state using different sensors, to perform reasoning about potential collisions, and to trigger safety functions upon that information. Accident analysis is used in a data model to represent uncertain knowledge and to perform reasoning. A simulation concept based on real accident data is introduced to evaluate the presented system concept.