Performance Evaluation of a Bernoulli filter Based Multi-Vehicle Cooperative Object Detection

Abstract

Abstract The paper presents the performance evaluation of a multi-sensor object detection algorithm applied in traffic situation. The chosen data fusion and estimation procedure is the Bernoulli particle filter, which is ideal for cooperative object detection, as it can handle the varying number of sensor measurement and object appearance-disappearance too. The simulated detectors are automotive radar sensors, capable of measuring object speed, distance and bearing. The filter performance is examined along four aspects: number of particles used, simulation timestep, sensor noise and object motion model noise. The applied noise models represent the quality of the equipped sensors and our lack of knowledge on object motion, therefore the robustness of the filter can be examined by varying these parameters. The amount of particles and the used timestep effect the demanded computational power and also the estimation error, hence the scaling of the algorithm can be measured. In order to obtain a statistically meaningful result, a batch of 50 runs for every parameter set were used.