Almost every 6th accident involving a passenger car with injured occupants shows skidding in the pre-crash phase despite modern ESP systems (source: GIDAS). Causes for this include inappropriate driver intervention in safety-critical driving situations or a slippery road surface due to snow and ice. Skidding represents a special, non-linear vehicle motion, which strongly depends on external environmental conditions (e.g. friction coefficient of the road surface).
In order to be able to activate future safety systems also in these driving conditions by forward looking sensors, the near vehicle environment must be reliably and continuously detected. In addition, the activation of passive safety systems (e.g. airbags) represents a safety-critical decision and is classified the highest Automotive Safety Integration Level (ASIL). Therefore, to increase robustness and safety, independent plausibility methods of environment information from forward looking sensors are developed and researched with new methods of object detection and tracking in critical driving situations. These methods are implemented on prototype vehicles and tested under real environmental conditions in C-ISAFE.