Performance Study of Object Tracking with Multiple Kalman Filters in Autonomous Driving Systems

Object tracking is an important and central aspect of autonomous driving, as it underlies the obstacle detection and avoidance systems of any type of autonomous vehicles. A widely used method for tracking is based on Kalman filters, both for linear and non-linear cases, with different computational burden. Unfortunately, object tracking algorithms are computationally intensive, and they may not easily meet the efficiency and responsiveness requirements of real-time applications such as autonomous driving. This issue motivates ad-hoc investigations to speed up the computation and make Kalman filtering available even within limited computational power. This paper carry out a performance evaluation of a Kalman filter based object tracking system taken from a real tramway use-case, and aims at improving its performance efficiency by leveraging parallelization. In particular, this work analyzes the possibilities of execution parallelization on multi-core processors, proposing a target-specific optimization approach and comparing the obtained results, then summing them in general lessons learned. Our technique achieves up to 80% reduction of single frame processing time in the most crowded cases.