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Environmental perception is a prerequisite for autonomous driving and also a challenging task particularly in cluttered dynamic environments such as complex urban situations. In this paper, we present a robust algorithm for Multi-Target Tracking (MTT) using a Velodyne 3D HDL-64 Lidar sensor. The main contribution of this paper is a practical framework for selecting and representing useful information from the sensor raw data. Since the sensor produces a huge amount of data, a perception algorithm cannot be carried out in real-time without simplifying the sensor information. Unlike prior works, we introduce hybrid ground classification and the Region of Interest (ROI) identification method in order to filter out the amount of unwanted raw data for the actual tracking. And the environment is also abstracted based on an occupancy grid map. Moreover, we introduce feature based object geometry for precise estimation of the system state. In contrast to prior approaches, which use object geometry for the classification, we use it in order to compensate the unintended dynamics caused by shape change or occlusion. Our proposed MTT algorithm is able to run in real-time with an average processing time of 20ms. We evaluate it using our experimental vehicle "Leonie" in complex urban scenarios.

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