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Session B3: Sensor-Fusion for GNSS-Challenged Navigation

Infrastructure-Assisted Cooperative State Estimation of Ego-Vehicle via Augmentation of Asynchronous Kinematic Measurements
Saswat Priyadarshi Nayak, Guoyuan Wu, Matthew Barth, UC Riverside; Yongkang Liu, Emrah Akin Sisbot, Kentaro Oguchi, Toyota Motor North America R&D
Location: Beacon A
Date/Time: Wednesday, Jan. 24, 5:08 p.m.

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.

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