Automated vehicle control requires full knowledge of motion behavior of the preceding target vehicles (PTVs), and the states such as longitudinal/lateral velocity and yaw rate are critical for the PTV behavior description. However, the PTV's lateral states estimation have seldom been addressed in the state-of-the-art literatures. Aimed at providing reliable PTV lateral states, this paper presents a novel combined model-based estimation scheme. Different from the conventional PTV models, the proposed model is constructed based on the host-target vehicle dynamics and road constraints. Specifically, steering angle of the PTV is included in the state vector. The measurements, such as heading angle, road curvature, and lateral distance to the lane center, are available from an onboard vision system. As a vision system inevitably has measurement delay, a modified Kalman filter is developed to address the sampling issue. To verify the proposed approach, hardware-in-the-loop experiments are conducted in designed testing scenarios.
|Number of pages||10|
|Journal||IEEE Transactions on Industrial Informatics|
|Early online date||18 Apr 2018|
|Publication status||Published - Sep 2018|
Bibliographical notePublisher Copyright:
© 2005-2012 IEEE.
Copyright 2018 Elsevier B.V., All rights reserved.
- Automated vehicle
- lateral velocity
- measurement delay
- preceding target vehicle (PTV)
- state estimation
- yaw rate
ASJC Scopus subject areas
- Control and Systems Engineering
- Information Systems
- Computer Science Applications
- Electrical and Electronic Engineering