Development of an embedded vehicle safety system for frontal crash detection

Abstract

Automobiles crashes are becoming a source of increasing social concern, which instigates government bodies to impose stringent requirements on vehicle safety. The development of the embedded intelligent safety system (ISS) platform is a difficult task and involves many factors such as generation and interconnection of the hardware that support the execution of software. This paper deals with high-performance computing to detect and monitor frontal vehicle crash using a sensing algorithm which has been embedded in the proposed vehicle safety system for frontal collision. It also describes the mechanism for generating the frontal collisions data using a pneumatic device. The crash characteristics of precision collision, detection algorithm and computational model enhances the sensing algorithm that is embedded for real-time application. The embedded structure is developed based on a hardware platform, a system interface and a software driver. In this work, the artificial crash data generated using the pneumatic device are used for three main purposes. First, it is used to validate the effectiveness of the proposed sensing algorithm. Secondly, we use it to evaluate the performance of the ISS for detection and lastly, we use the data to monitor the effectiveness of the developed embedded safety in an automotive application.

Keywords:

• vehicle crash detection
• sensing algorithm
• embedded intelligent safety system

Acknowledgement

The authors would like to thank the Malaysian Ministry of Science, Technology and Innovation (MOSTI) for funding this work through IRPA research grant 03-02-02-0017-SR0003/07-03.