The conventional detection method of a side crash is using either a pressure sensor located on the door or an acceleration sensor, also referred to as G sensor. These sensors detect body intrusion in a side crash.

This paper focused not only on intrusion of body but also on vehicle behavior change, which is detected simultaneously with body intrusion in a side crash. Using intrusion and behavior change of vehicle, an investigation of side crash detection performance was conducted.

Two methods were devised to detect vehicle behavior change in a side crash. One method is using yaw-rate sensor located at the center of the vehicle, and the second method is using a G sensor, which has a sensitivity axis in the longitudinal direction of the vehicle and located on the body side.

A side crash detection algorithm was also devised which combined G sensor of lateral direction, which detects lateral accelerations in a side crash, and a yaw-rate sensor or G sensor of longitudinal vehicle direction, which detects other changes to the impacted vehicle other than lateral accelerations, referred to in this study as vehicle behavior.

This research sought to determine whether crash detection performance can be satisfied for various crash modes using numerical simulations.

The results of these numerical simulations indicate that G sensor response time is fast which makes it effective in detecting a high speed crash. The results also showed that yaw-rate data is stable, which implies that data is reliable, allowing the use of the developed crash detection algorithm for predicting vehicle behavior changes, within certain speed limits.

Moreover, a side crash test using a test vehicle, also referred to in this paper as Complete Body Unit or CBU, CBU was also completed and confirmed that body intrusion and vehicle behavior change occur simultaneously and can be reasonably detected a side crash using this paper’s crash detection algorithm. This could potentially transform side crash detection in the automotive industry.