Uncertainty Aware EKF: a Tracking Filter Learning LiDAR Measurement Uncertainty

In this paper, an extended Kalman filter (EKF) framework, called uncertainty aware EKF (UA-EKF), is developed by utilizing contextual knowledge to improve the vehicle tracking accuracy for autonomous vehicles. The proposed framework can learn and estimate the uncertainty associated with the measurements provided by a LiDAR-based vehicle detection algorithm. The UA-EKF has two major parts: one has the ability to estimate the state-dependent measurement noise's statistics for LiDAR object detections, and the other is to create multiple-hypothesis measurements based on the detected vehicle's heading. The measurement uncertainties are learned based on the EKFNet, which is an algorithm that can learn the system noise covariance from measurement data. Both the learned noise statistics and multiple-hypothesis estimators are used to compensate the physical limitations of the LiDAR measurements. A detailed analysis of the measurement uncertainty and the methods to improve tracking performance during filtering are provided for the UA-EKF. The obtained results by using the nuScenes datasets show that estimating the measurement uncertainty is an efficient solution for tracking the vehicle based on LiDAR detections.