Infrastructure-Assisted Cooperative State Estimation of Ego-Vehicle via Augmentation of Asynchronous Kinematic Measurements

Saswat Priyadarshi Nayak, Guoyuan Wu, Matthew Barth, Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi

Abstract:	Connected and Automated Vehicle (CAV) research has gained significant popularity in the last decade. Vehicle state estimation is a key component in realizing safety-related CAV applications that requires ’where-in-lane’ level positioning accuracy ( < 0.3 meters). Vehicle-to-everything (V2X) information can assist conventional Global Navigation Satellite System (GNSS) positioning. In particular, the Infrastructure-to-vehicle (I2V) positioning solution can decrease dependency on vehicular sensors by leveraging infrastructure-sensed information. I2V-based positioning and tracking require a fusion of infrastructure and on-vehicle sensor measurements to track a vehicle cooperatively. This requires synchronization of multi-sensor measurements which are often multi-rate and asynchronous in nature. This paper addresses the problem by augmenting measurements from multiple distributed sensors. Infrastructure-based LiDAR-detected vehicle position measurements are fused with on-board GNSS position and odometry-speed measurements for vehicle state estimation. Additionally, the state estimation is improved through the implementation of an elliptic validation gate and Zero Velocity Update (ZUPT) during the filtering process.
Published in:	Proceedings of the 2024 International Technical Meeting of the Institute of Navigation January 23 - 25, 2024 Hyatt Regency Long Beach Long Beach, California
Pages:	1103 - 1116
Cite this article:	Nayak, Saswat Priyadarshi, Wu, Guoyuan, Barth, Matthew, Liu, Yongkang, Sisbot, Emrah Akin, Oguchi, Kentaro, "Infrastructure-Assisted Cooperative State Estimation of Ego-Vehicle via Augmentation of Asynchronous Kinematic Measurements," Proceedings of the 2024 International Technical Meeting of the Institute of Navigation, Long Beach, California, January 2024, pp. 1103-1116. https://doi.org/10.33012/2024.19537
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