A System Implementation and Evaluation of a Cooperative Fusion and Tracking Algorithm based on a Gaussian Mixture PHD Filter

This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and real vehicles endowed with automotive-grade sensors. We have extended our previously presented cooperative sensor fusion algorithm with a fusion weight optimization method and implemented it on a vehicle that we denote as the ego vehicle. The algorithm fuses information obtained from one or more vehicles located within a certain range (that we call cooperative), which are running a multi-object tracking PHD filter, and which are sharing their object estimates. The algorithm is evaluated on two Citroen C-ZERO prototype vehicles equipped with Mobileye cameras for object tracking and lidar sensors from which the ground truth positions of the tracked objects are extracted. Moreover, the algorithm is evaluated in simulation using simulated C-ZERO vehicles and simulated Mobileye cameras. The ground truth positions of tracked objects are in this case provided by the simulator. Multiple experimental runs are conducted in both simulated and real-world conditions in which a few legacy vehicles were tracked. Results show that the cooperative fusion algorithm allows for extending the sensing field of view, while keeping the tracking accuracy and errors similar to the case in which the vehicles act alone.