Vehicle and object models for robust tracking in traffic scenes using laser range images

Abstract

Detection and modeling of dynamic traffic scenes around a, driving passenger car is the long-term aim of the research project ARGOS at the University of Ulm. Each object close to the own car should be modeled and tracked using a specific individual dynamic model. The object classification is based on the geometric outlines and the dynamic behavior. For any sensor combinations usable to detect the environment, the velocity of the objects can be measured relatively to the movement of own vehicle. To. get the absolute velocity of the objects, the motion of the own vehicle must be measured for which the well know bicycle model is used. This ego-model is fed by sensor signals provided anyway by ABS, ASR or ESP. To eliminate the own motion from the object measurements, several coordinate transformations are required in the different stages of data processing. A proposal is given on how to solve this problem when using a laser range finder as a sensing device. Moreover, a simple object model is introduced for this task in order to save processing power. The algorithms can extended towards a multihypothesis approach which will result a more robust classification and tracking algorithm.