Receiver Autonomous Integrity Monitoring of GNSS Signals for Electronic Toll Collection

Abstract

Various Road User Charging (RUC) mechanisms are used to control the traffic and its resulting pollution, as well as revenue sources for reinvestment in the road infrastructure. Among them, Electronic Toll Collection (ETC) systems based on user positions estimated with Global Navigation Satellite Systems (GNSS) are particularly attractive due to their flexibility and reduced roadside infrastructure in comparison to other systems like tollbooths. Because GNSS positioning may be perturbed by different errors and failures, ETC systems, as liability critical applications, should monitor the integrity of GNSS signals in order to limit the use of faulty positions and the consequent charging errors. The integrity monitoring systems have been originally designed for civil aviation, so they need to be adapted to the ETC requirements. This paper studies the use of Receiver Autonomous Integrity Monitoring (RAIM), which are algorithms run within the GNSS receiver, and therefore easier to tune to ETC needs than other systems based on external information. The Weighted Least Squares Residuals (WLSR) RAIM used in civil aviation is analyzed, and an algorithm modification for ETC is proposed. Simulations demonstrate that the proposed RAIM algorithm has a superior level of availability over civil aviation based RAIM procedures, particularly in urban environments.

GPS Signal Authentication From Cooperative Peers

“…In addition to traffic monitoring, toll collection is also adopting GPS technology and benefits from GPS data. A notable example is the Toll Collect Project, which has operated in Germany since 2005 [7], [8]. Using GPS to identify when a vehicle is on a tolled road, this system outperforms traditional toll gates in terms of wide-area coverage and flexible toll fee calculation [9].…”

SLAM-based Integrity Monitoring Using GPS and Fish-eye Camera

“…5. Finally, utilizing the estimated fault mode and overall state vector, we formulate the failure slope for the Graph-SLAM framework and subsequently, compute the protection levels using worst-case failure mode slope analysis [15,16]. The proposed SLAM-based IM algorithm using GPS and fish-eye camera consists of three main modules, namely measurement pre-processing, extended graph optimization and IM for Graph-SLAM.…”

“…where k denotes the number of redundant measurements, i.e., difference between the number of overall measurements, denoted by n and overall states, denoted by l, such that k = n − l. λ indicates the non-centrality parameter associated with the overall test statistic during faulty conditions. According to the worst-case failure mode slope analysis [15], as seen in Fig. 4, the protection level is calculated as the projection in the position domain of the measurement faults that would generate a non-centrality parameter λ = λ th in the overall test statistic ζ with the maximum slope.…”

“…In particular, the non-centrality parameter λ th is estimated from the false-alarm, denoted by p F A and mis-detection rates, denoted by p M D , which are set according to the pre-defined integrity requirements. Figure 4: Protection levels computed as the intersection of worst-case failure mode slope and non-centrality parameter [15].…”

“…We propose SLAM-based IM algorithm using GPS and fish-eye camera to compute the error bounds, termed as protection levels, of the estimated navigation solution by applying worst-case failure slope analysis [15,16] to the Graph-SLAM framework. In this work, we consider global landmarks as the GPS satellites and additional local landmarks as the key image pixels in the world frame.…”