Evaluation of Sensor Tolerances and Inevitability for Pre-Crash Safety Systems in Real Case Scenarios

Abstract

Vehicle safety is an enabler of Automated Driving. The combination of active and passive vehicle safety can further increase the safety level of vehicle occupants. With integrated safety systems predicting inevitable crashes and the corresponding crash constellation, the activation of irreversible restraint systems like airbags will allow better crash mitigation and new interior concepts. One requirement is a comprehensive methodology to ensure the correct detection of the current traffic situation, the involved vehicles, and the collision inevitability. This paper presents a novel approach for crash evaluation in the pre-crash phase based on sensor fusion using camera and LiDAR for bullet vehicle detection in combination with physical motion-model-based collision detection. Urban intersection scenarios with typically severe side crashes are investigated using this methodology. The presented method can also be applied to investigate other traffic scenarios. One focus of this paper is the effect of sensor tolerances, which lead to inaccurate object data on the prediction of the inevitability of the crash. The analysis proves the potential of preemptive activation of airbag systems.