Research on Roadside Unit-Assisted Cooperative Positioning Method for a Connected Vehicle Environment

Abstract

With the rapid development of intelligent transportation systems and connected vehicle (CV) technology, vehicle-to-infrastructure communication technologies have provided new solutions to traditional traffic safety and efficiency issues. However, the current intelligent CVs often provide positioning services only through a single GPS. These modules’ positioning accuracy can be insufficient to support the safety and reliability of security applications. The question arises of how to enhance GPS positioning accuracy in a CV environment without adding additional equipment and using only the information that existing CV devices can access. This paper proposes a roadside unit (RSU)-assisted GPS-RSS (received signal strength) cooperative positioning method for a CV environment. The rough position information from GPS is combined with RSS ranging and dead reckoning to obtain preliminary position estimated coordinates of the CV. Bayesian filtering is performed to obtain a more accurate preliminary position estimate. The final position estimated coordinates, obtained after data fusion, are combined with the high-precision map data (MAP) sent by the RSU to match the lane where the vehicle is located. Simulation and field tests verify each other, and the results show that the lane positioning accuracy of GPS can be improved by 21% within the range from the RSU to the CV’s on-board unit.

Roadside Units-Enabled Cooperative Localization Scheme for Autonomous Vehicles Under Gnss-Denied Environments: Methodology and Demonstration

“…5. As expected based on previous research by Liu et al [18], positioning error gradually increases with increasing vehicle speed for all methods but at varying rates. The LS-based methods (LS-TS and LS-EKF) show nearly exponential growth in errors with increasing speed, indicating poor adaptation to high-speed conditions.…”

“…Accurately determining positioning relies heavily on signal propagation modeling. Various wireless communication channel models are available, including the Okumura-Hata model, Lee model, standard indoor model, attenuation factor model, linear loss model, and logarithmic path loss model [18]. Among them, the logarithmic loss model holds that path loss of signal propagation is logarithmically related to the increase in distance, and can be considered as a simple and accurate description of signal propagation characteristics in traffic scenarios [18], [21], [24], [43], [44].…”

“…As a result, obtaining accurate ground truth for comparing different localization methods is challenging. To address this issue, we followed a similar approach to Liu et al [18]. We took pictures of the surrounding environment (including buildings and landmarks) near RSUs and crossreferenced them with Google Street View to obtain their coordinates on Google Maps accurately.…”

Research on localization and navigation methods for intelligent terminal devices oriented to multi-source fusion technology

“…Positioning technology means that positioning is achieved by collecting some kind of information, such as electromagnetic information, image information, etc., in the vicinity of the point to be measured. Usually, the information in the vicinity of the point to be measured makes it redundant, and selecting multiple information for fusion localization can improve the accuracy of localization [20][21][22][23]. Therefore, the accuracy and stability of the localization capability are two basic issues.…”

Emitter Geolocation from Near-Space Platform Using 3D TDOA

“…In recent years, research has focused on the geolocation of an emitter from electromagnetic emissions, which is largely employed in reconnaissance, surveillance, and intelligence gathering. In the fields of aeronautics and astronautics [1] [2], near-space [3] [4] has emerged as a new research hotspot. Figure 1 shows the near space platform comprising of planes, ballons and UAVs.…”