Abstract

dvance Driver Assistance Systems designed to enhance safety require a perception module that should be reliable, robust and affordable. We propose a new cooperative fusion method between two exteroceptive sensors for the detection and tracking of obstacles. Focus is cast on the issue of data association of asynchronous measurements from multiple sensors of different nature. This perception and detection module is applied to local perception embedded on a host vehicle (the ego-vehicle) with two types of complementary sensors (laser scanner and mono-camera). The first detection stage is performed by the mono-layer laser scanner which provides a set of clustered impact points. Those clusters are filtered and projected into the image to define targets. Detected vehicles are tracked using an image registration algorithm. A multi-objects association and tracking algorithm based on belief theory is implemented to estimate the dynamic state of the tracks and to monitor appearance and disappearance of obstacles. Our approach does not make any assumption on the type of driving maneuver and is able to address highly non linear dynamic configuration. The method is applied on both real data and in simulated environment for the validation stage.