Tracking and data association
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
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Autonomous Vehicle Following can be achieved if the poses of both the follower and leader vehicles are continuously estimated. This can be achieved by using a Bayesian estimation technique together with a virtual trailer link model. The advantage of such a model is that the follower vehicle will trail a virtual trailer, modeled as an attachment to the leader vehicle, instead of the leader vehicle itself, so that a safe spacing between the two vehicles is guaranteed. The key to a tractable solution to this vehicle following problem is the justifiable assumption that the pose of the follower vehicle is statistically independent of that of the leader. This assumption is valid when conditioned on the history of the follower vehicle's inputs and the sensor observations made by the follower vehicle. Hence, a factored solution to the joint estimate of the follower and leader poses can be formulated. Due to the factored solution, the pose of the follower vehicle is estimated separately using a recursive estimator. In a separate estimator, the poses of the virtual tailer and the leader vehicle are augmented in the tracking process of the leader vehicle. The aim is to command the follower vehicle to trail the estimated pose of the virtual trailer link model. The pose of the virtual trailer is computed with an on-board sensor mounted on the follower vehicle. A case study on the implementation of the proposed formulation, using an Extended Kalman Filter as the main estimator, is presented. First, simulation results are presented. To make the simulation results comparable to the actual system, the variances of sensor measurements are set according to real sensor data-sheet values. Various types of vehicle maneuver, such as straight paths and clothoids with left and right transition paths, are considered. Real experiments are also carried out in a car park. A comparison of the estimated paths and the best available ground truth is presented. The path deviations of the proposed system are also compared with similar systems in the literature.