Detection of the farm road positioning scene classification for unmanned driving based on GNSS

The positioning environment of farm roads is complex and variable, with defined scenes including open sky, forest, overpass, and tunnel. Relying solely on the Global Navigation Satellite System (GNSS) for positioning throughout the operation of unmanned agricultural machines is insufficient. This study addressed the need for rapid and accurate identification of farm road scene types to select appropriate positioning devices for unmanned agricultural machines. Real-time satellite signal data, obtained through onboard GNSS receivers and combined with broadcast ephemeris data, were used to extract positioning status and track satellite statistics and distribution features. A classifier based on a sliding window was developed, and an inference based on tracked satellite numbers was proposed to detect the farm road positioning scene and calculate the length of different road segments. This study was conducted using real-world working conditions at an agricultural machinery cooperative in Miyun, Beijing, China, where three sets of GNSS data were collected from the farm road using a vehicle-mounted all-frequency GNSS receiver. The results showed that the classification accuracy for open-sky, overpass, and tunnel scenes was 93.96%, with a recall rate of 98.31% and an F1 score of 95.82%. Compared with random forest and XGBoost, the F1 scores improved by 17.83 and 5.70, respectively. This method, based on GNSS multi-feature fusion, can provide a reference for selecting multi-source positioning devices and planning the routes of unmanned agricultural machines.