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<p>With the development of autonomous driving and intelligent transportation systems, vehicle navigation accuracy has become a critical focus. The policy of charging highway taxes based on mileage, which China is preparing to implement, will require most vehicles to be equipped with low-cost Global Navigation Satellite System (GNSS) receivers. These receivers will collect vehicle location and mileage data, which will serve as the basis for toll collection. However, in challenging urban environments such as tunnels, interchanges, and city canyons, GNSS signals may be obstructed or unavailable, leading to degraded positioning accuracy and reduced trustworthiness, which may cause inaccurate billing and related controversies. To address this issue, we design and implement a roadside unit (RSU) system equipped with LiDAR and cameras to assist vehicle localization. The RSU detects vehicles using LiDAR and cameras and broadcasts the estimated positions to vehicles via LTE-V. The vehicle then fuses RSU-provided positions with its onboard inertial measurement unit (IMU) using an error-state Kalman filter (ESKF). To further enhance precision and quantify the positioning trustworthiness, a real-time error model is proposed to predict the position-error covariance for each RSU detection, which is then used in the filtering process. We construct a hardware prototype to validate the framework and conduct extensive experiments. Results demonstrate that the proposed approach achieves an accuracy of 0.19m (1<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\sigma\)</EquationSource> </InlineEquation>), and the real-time error model further improves the navigation performance to 0.16m (1<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\sigma\)</EquationSource> </InlineEquation>).</p>

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Roadside-unit-assisted vehicle positioning with real-time error model in GNSS-denied environment

  • 期刊论文

  • 发表日期：

    2026年2月26日

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  • Jiaye Yang,
  • Xin Zhang,
  • Xingqun Zhan

摘要

With the development of autonomous driving and intelligent transportation systems, vehicle navigation accuracy has become a critical focus. The policy of charging highway taxes based on mileage, which China is preparing to implement, will require most vehicles to be equipped with low-cost Global Navigation Satellite System (GNSS) receivers. These receivers will collect vehicle location and mileage data, which will serve as the basis for toll collection. However, in challenging urban environments such as tunnels, interchanges, and city canyons, GNSS signals may be obstructed or unavailable, leading to degraded positioning accuracy and reduced trustworthiness, which may cause inaccurate billing and related controversies. To address this issue, we design and implement a roadside unit (RSU) system equipped with LiDAR and cameras to assist vehicle localization. The RSU detects vehicles using LiDAR and cameras and broadcasts the estimated positions to vehicles via LTE-V. The vehicle then fuses RSU-provided positions with its onboard inertial measurement unit (IMU) using an error-state Kalman filter (ESKF). To further enhance precision and quantify the positioning trustworthiness, a real-time error model is proposed to predict the position-error covariance for each RSU detection, which is then used in the filtering process. We construct a hardware prototype to validate the framework and conduct extensive experiments. Results demonstrate that the proposed approach achieves an accuracy of 0.19m (1 \(\sigma\) ), and the real-time error model further improves the navigation performance to 0.16m (1 \(\sigma\) ).