• sensor fusion
• polar grid map
• multi target tracking
• multi hypothesis tracking
• Kalman filter
• dynamic targets
• data association
• autonomous driving
• slow moving vehicles
• static targets

An important requirement for autonomous driving, is to detect correctly static targets and dynamic targets under various state of motion.
The opportunity of detecting that depends upon the availability and reliability of different sensor data processed by the sensor fusion module. This thesis uses data from laser, radar and stereo-camera with built-in tracking modules and the objective is to make the resultant of two different sensor fusion modules, sharing the same sensor data, to be consistent accordingly to their operational requirements. One sensor fusion module deals with dynamic targets providing an L-shape representation, while the other module is built to represent only static targets by means of contour polylines. Due to uncertainty in detecting static targets, a clear differentiation between standing objects and vehicles moving at slow speed is not possible for the second module. Slow moving objects can then be considered as standing objects and wrongly represented by the perception system. The aim of this thesis is then to provide a better representation of vehicles moving at slow speed. This is done, at first, adapting the contour representation for moving objects. Then, data association techniques between the two modules are investigated and a novel approach based on multi-hypothesis tracking is presented. The results are evaluated using simulations and validated with real world data and ground truth.