High Accuracy Positioning Engine with an Integrity Layer for Safety Autonomous Vehicles

Tijero, E. Domínguez; Fernández, L. Martínez; Zarzosa, J. I. Herrero; García, Javier; Ibañez‐Guzmán, Javier; Stawiarski, Emmanuel; Xu, Philippe; Avellone, Giuseppe; Pisoni, Fabio; Falletti, Emanuela; Ortiz, Miguel

doi:10.33012/2018.15843

Cited by 6 publications

(2 citation statements)

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“…For KIPL, the influence of all measurement inputs to the sensor data fusion are modeled dynamically and fused to a total error distribution which is used to compute the PL [ 38 ]. Results of KIPL in the ESCAPE research project are presented by Tijero et al [ 39 ].…”

Section: State Of the Artmentioning

confidence: 99%

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Gottschalg

Leinen

2021

Sensors

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High-integrity information about the vehicle’s dynamic state, including position and heading (yaw angle), is required in order to implement automated driving functions. In this work, a comparison of three integrity algorithms for the vehicle dynamic state estimation of a research vehicle for an application in automated driving is presented. Requirements for this application are derived from the literature. All implemented integrity algorithms output a protection level for the position and heading solution. In the comparison, four measurement data sets obtained for the vehicle dynamic state estimation, which is based on a Global Navigation Satellite Signal receiver, inertial measurement units and odometry information (wheel speeds and steering angles), are used. The data sets represent four driving scenarios with different environmental conditions, especially regarding the satellite signal reception. All in all, the Kalman Integrated Protection Level demonstrated the best performance out of the three implemented integrity algorithms. Its protection level bounds the position error within the specified integrity risk in all four chosen scenarios. For the heading error, this also holds true, with a slight exception in the very challenging urban scenario.

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Section: State Of the Artmentioning

confidence: 99%

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Gottschalg

Leinen

2021

Sensors

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“…The integrity is a measure of the trust that can be placed in the correctness of the information supplied by a navigation system, including the ability of the system to provide timely warnings about when it should not be used. For safety-of-life applications, such as civil aviation [1] and intelligent transport systems (ITSs) [2,3], the integrity is of paramount importance. In civil aviation, different integrity monitoring systems are available for different flight phases, including receiver autonomous integrity monitoring (RAIM), space-based augmentation systems (SBASs), and ground-based augmentation systems (GBASs).…”

Section: Introductionmentioning

confidence: 99%

RAIM Fault Detection and Exclusion with Spatial Correlation for Integrity Monitoring

Jiang

2022

Remote Sensing

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For safety-of-life applications used together with global navigation satellite systems, such as in civil aviation and autonomous driving, integrity is of paramount importance. Integrity monitoring protects the user safety based on two general approaches: mitigating large signal failures and bounding the residual errors. The fault detection and exclusion (FDE) of faulty satellites is part of the former approach. In the classical integrity monitoring algorithms in civil aviation, the use of test statistics based on least squares residuals relies on the assumption that the observations from different satellites are independent. When applied to urban environments with spatial correlation introduced by large multipath errors, a review of the FDE method is needed. With the optimal FDE method defined as the one with minimized integrity risk, we propose two optimization criteria for use in fault detection and fault exclusion. The optimal test statistics were obtained by analytical derivation for cases with and without correlations among different satellites. This method was theoretically compared with another commonly used test statistic using the minimum detectable bias, and it was numerically compared using the horizontal protection level under the scenario of advanced receiver autonomous integrity monitoring. The optimal test produces less conservative protection level results, and its advantage is especially obvious when the geometries are weak, or when the correlation coefficients among the satellites are high.

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Robust Sequential Integrity Monitoring for Positioning Safety in GNSS/INS Integration

Shao,

Yu,

Zhang

et al. 2024

IEEE Sensors J.

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High Accuracy Positioning Engine with an Integrity Layer for Safety Autonomous Vehicles

Cited by 6 publications

References 1 publication

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

RAIM Fault Detection and Exclusion with Spatial Correlation for Integrity Monitoring

Robust Sequential Integrity Monitoring for Positioning Safety in GNSS/INS Integration

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