Sparse Multi-sensor Monitoring System Design for Vehicle Application

In today's fast growing vehicle industry, the number of functionalities (comfort features, monitoring features, safety features, etc.) is steadily increasing. Each of these functionalities are developed independently from each other, hence the sensors are not shared among them. Although this design approach results into robust monitoring of these different functionalities, it requires a large number of sensors in different locations resulting in a complex hardware and software architecture (e.g. complex wires). This paper describes our approach where a multi sensor design method is used to optimally select locations of sensors that are shared by different functionalities. This results into a reduced number of sensors that monitor the same amount of functionalities. We demonstrate in this paper, an optimization algorithm based on Multi-Objective Integer Programming (MOIP) for optimal sensor placement for monitoring Motion Sickness Dose Value (MSDV) estimation and Speed Bump Detection (SBD) as part of a driver assistant system. The algorithm is further validated on a numerical data-set captured from an IPG CarMaker vehicle model. The methodology can be further extended to more functionalities with large number of applications in vehicle industry.