An Integrated Smart Road Stud-Based Vehicle Localization Method With Velocity Estimation

The integration of the global navigation satellite system (GNSS), odometer, and inertial navigation system (INS) holds significant potentials for achieving high-precision vehicle localization. However, GNSS is vulnerable to obstructions and jamming, and the odometer is unreliable in harsh road conditions. These factors can lead to cumulative positioning errors in GNSS-denied environments. To address these issues, a novel multi-source information fusion based vehicle localization method that integrates an onboard binocular camera, an INS, and smart road studs—Internet of Things (IoT) devices extensively used for road safety and data collection in intelligent transportation systems— is introduced. We construct a position measurement model directly in the camera coordinate system through an enhanced You Only Look Once 8th version (YOLOv8) algorithm for smart road stud detection, combined with binocular vision measurement and position transformations. Additionally, we propose a method to enhance vehicle localization accuracy by integrating vehicle speed without relying on additional hardware speed sensors. The vehicle’s speed is estimated from the image sequences captured by the onboard camera using a deep neural network (DNN), named Speed-Net. The final navigation results are produced by fusing the smart road stud aided positioning information, the estimated vehicle speed, and INS data through an error-state extended Kalman filter (ESEKF). Real-world experiments demonstrate the effectiveness of the proposed Smart road stud (SRS)/Velocity/INS integrated vehicle localization method.