An Information Theoretic Approach for Assessing the Performance of Vehicle Kinematic Tracking

Estimating the position, velocity and orientation of a vehicle is an extremely important aspect of highly assisted and autonomous driving scenarios. As a result of decades of research into this topic, there exist many tracking algorithms, each with different operating principles driven from different statistical frameworks. However, due to the complexity of the applications with which they are applied to, no algorithm has sufficient generality to be applied in all circumstances. While the topic of assessing the performance of algorithms has been investigated in the past, there exists no standardized framework for comparing the performance of different algorithms. In this paper we introduce an information theoretic framework which uses the Kullback Leibler Divergence to consider the relative information gain between different fusion algorithms. This framework is independent of the sensor systems and trajectories and considers only the technical operation of the algorithms. The results presented in this paper illustrate the utility of this approach and provide valuable insight for the development of algorithmic methodologies for real world vehicle dynamics estimation.