Extended Object Tracking Performance Comparison for Autonomous Driving Applications

Extended object tracking is crucial for autonomous driving, as it enables vehicles to perceive and respond to their environment accurately by considering an object’s shape, size, and motion over time. Two commonly used methods for extended object tracking, Joint Probabilistic Data Association (JPDA) and Gaussian Mixture Probability Hypothesis Density (GM-PHD), were compared in autonomous vehicles using radar data. Both JPDA and GM-PHD perform well in tracking multiple extended objects, but GM-PHD demonstrates a performance advantage, especially in terms of the Generalized Optimal Sub-Pattern Assignment (GOSPA) metric, which measures the accuracy of tracked object positions in comparison to their actual positions.