Design of an Adaptive Kalman Filter for Autonomous Vehicle Object Tracking

Abstract

Tracking objects in the surrounding environment is a key component of safe navigation for autonomous vehicles. An accurate tracking algorithm is required following object identification and association. This thesis presents the design and implementation of an adaptive Kalman filter for tracking objects commonly observed by autonomous vehicles. The design results from an evaluation of motion models, noise assumptions, fast error convergence methods, and methods to adaptively compensate for unexpected object motion. Guidelines are provided on these topics. Evaluation is performed through Monte Carlo simulation and with real data from the KITTI autonomous vehicle benchmark. The adaptive Kalman filter designed is shown to be capable of accurately tracking both typical and harsh object motions.