A pedestrian detection system based on thermopile and radar sensor data fusion

Automotive pedestrian protection systems will be introduced in the EU in short term to reduce the number of accidents and injury fatalities. As with any safety issue, a comprehensive approach comprising both active and passive safety elements should be followed and it is also valid for pedestrian protection. Passive safety short term solutions can be contact sensor systems that trigger raisable engine hoods. This paper discusses an innovative approach for pedestrian detection and localization, by presenting a system based on two short range radars and an array of passive infrared thermopile sensors, aided with probabilistic techniques for detection improvement. The two short range radars are integrated in the front bumper of the test vehicle. They are able to observe and track multiple targets in the region of interest. However, one difficulty is to distinguish between pedestrians and other objects. Therefore, a second sensor system is required to classify pedestrians reliably. This system consists of spatial distributed thermopile sensors which measure the object presence within their respective field-of-view independently. These measurements are then validated and fused using a mathematical framework. Thermopiles are excellent to detect the thermal radiation emitted by every human. However, a robust signal-interpretation algorithm is mandatory. In this work a statistical approach combining Dempster-Shafer theory with occupancy-grid method is used to achieve reliable pedestrian detection. Thermopile and radar sensors use independent signature-generation phenomena to develop information about the identity of objects within the field of view. They derive object signatures from different physical processes and generally do not cause a false alarm on the same artifacts. The integration of the sensor readings from the radar and thermopile system is conducted using unifying sensor-level fusion architecture.