Probabilistic signal interpretation methods for a thermopile pedestrian detection system

Whenever addressing pedestrian related injury focal points in automotive accidentology, a comprehensive approach comprising both active and passive safety elements should be followed. Passive safety short term solutions can be contact sensor systems that trigger raisable engine hoods and an active safety element could be the brake assist. However, an important enabler for a future pedestrian protection system is a suitable, low-cost, environment-friendly sensing technology for pedestrian detection, supported by a fast and reliable algorithm for object localization. This paper discusses such an innovative approach for pedestrian detection and localization, by presenting a system based on an array of passive infrared thermopile sensors, aided with probabilistic techniques for detection improvement. The distributed thermopile sensors (sensor-array) detect the object presence within their respective field-of-view independently from each other. These measurements are then validated and fused using a mathematical framework. The focus of this paper is on the signal interpretation of the thermopile sensors. Since passive thermopile sensors are prone to background influences and can detect only the relative temperature changes, a robust signal-interpretation algorithm is essential. In this respect, a statistical approach combining Dempster-Shafer-theory with occupancy-grid method is used to achieve reliable pedestrian detection. The performance of the proposed approach is discussed by presenting some experimental results.