Vehicle sensor-based pedestrian position identification in V2V environment
This thesis presents a method to accurately determine the location and amount of pedestrians detected by different vehicles equipped with a Pedestrian Autonomous Emergency Braking (PAEB) system, taking into consideration the inherent inaccuracy of the pedestrian sensing from these vehicles. In the thesis, a mathematical model of the pedestrian information generated by the PAEB system in the V2V network is developed. The Greedy-Medoids clustering algorithm and constrained hierarchical clustering are applied to recognize and reconstruct actual pedestrians, which enables a subject vehicle to approximate the number of the pedestrians and their estimated locations from a larger number of pedestrian alert messages received from many nearby vehicles through the V2V network and the subject vehicle itself. The proposed methods determines the possible number of actual pedestrians by grouping the nearby pedestrians information broadcasted by different vehicles and considers them as one pedestrian. Computer simulations illustrate the effectiveness and applicability of the proposed methods. The results are more integrated and accurate information for vehicle Autonomous Emergency Braking (AEB) systems to make better decisions earlier to avoid crashing into pedestrians.
Chen, Purdue University.
Statistics|Computer Engineering|Computer science
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