Abstract
This paper deals with the interface-relevant activity of a vehicle integrated intelligent safety system (ISS) that includes an airbag deployment decision system (ADDS) and a tire pressure monitoring system (TPMS). A program is developed in LabWindows/CVI, using C for prototype implementation. The prototype is primarily concerned with the interconnection between hardware objects such as a load cell, web camera, accelerometer, TPM tire module and receiver module, DAQ card, CPU card and a touch screen. Several safety subsystems, including image processing, weight sensing and crash detection systems, are integrated, and their outputs are combined to yield intelligent decisions regarding airbag deployment. The integrated safety system also monitors tire pressure and temperature. Testing and experimentation with this ISS suggests that the system is unique, robust, intelligent, and appropriate for in-vehicle applications.
Highlights
In any vehicle, the presence of intelligent safety implies an active system that promotes safety, security and driving comfort [1]
Methods and algorithms for the intelligent safety system (ISS) were developed for airbag deployment decision system (ADDS) and tire pressure monitoring system (TPMS), which involved the individual algorithms for occupant detection, classification and position based on weight sensing and image processing as well as for vehicle crash detection
To assess the performance of the ISS, we evaluated its network interface processing, its image and signal processing for the purpose of occupant detection, its classification and positioning, its vehicle crash detection accuracy, its severity analyses for ADDS and its TPMS performance monitoring
Summary
The presence of intelligent safety implies an active system that promotes safety, security and driving comfort [1]. To meet high expectations for control and safety, a large number of individual safety systems are required [2,3]. This has led to concern over safety issues and has resulted in a need for integrated ISSs that feature effective new technologies, characterize safety issues and provide solutions for monitoring, detecting, and classifying impending crashes or unsafe. Despite the success of some of these systems, occupant detection and classification involving human subjects and non-human objects still poses a number of challenges, and further progress remains necessary for addressing changes in illumination, image scale, image quality, expression and pose. Sensors for data acquisition, real time implementations, and operations should be studied further [8]
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