Almost every 6th accident of a passenger car with injured passengers shows a skidding in the pre-crash phase despite modern ESP systems (source: GIDAS). The causes of this are, for example, inappropriate driver interventions in safety-critical driving situations or a slippery road surface caused by snow and ice. Skidding is a special, non-linear vehicle movement which is strongly dependent on external environmental conditions (e.g. friction coefficient of the road surface).
In order to be able to activate future safety systems by predictive sensors in these driving conditions, the close vehicle environment must be reliably and continuously detected. In addition, the activation of passive safety systems (e.g. airbags) represents a critical decision and is classified among the highest Automotive Safety Integration Level (ASIL). To increase robustness and safety, independent plausibility check methods of the environment information from predictive sensors are therefore developed and researched with new methods of object detection and tracking in critical driving situations, implemented on prototype vehicles and tested under real environmental conditions in CARISSMA.