Abstract
In many transport applications, one of the safety critical function is the localization. This is all the more true for land transport applications such as autonomous vehicles. While the democratization of satellite positioning systems, such as GPS, Galileo, Beidou or Glonass, has made it possible to consider a global solution applicable anywhere in the world, the principle of positioning by receiving signals from satellites more than twenty thousand kilometers away shows limits when they are confronted with disturbances related to the environment close to the receiver. However, for these safety-critical applications, the requirements are strong and sometimes even conflicting. The developed function must meet a defined level of precision, availability, continuity of service, integrity, operational safety and finally robustness to environment changes. Taken separately, these requirements can be achieved by actions recommended by the literature. For more precision and availability, coupling between absolute GNSS data and relative INS and odometer data, is recommended. To increase safety and integrity, a fault detection layer is essential, but this will negatively impact availability. One therefore needs a fault management layer. A harmonious policy, thought at the function design, makes it possible to achieve all the objectives. In this study, we propose a framework based on a tripartite approach: the tight fusion of GNSS and IMU data, the development of a diagnostic layer based on information theory and using the very promising alpha Rényi divergence, as well as a fault isolation layer. The diagnostic layer is designed to be robust and adaptive to changing environment through a deep neural network. The proposed framework is tested on data acquired in the field. Encouraging results allow to consider the generalization of the concept.
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Data is the property of CRIStAL Laboratory and cannot be shared publicly at this time.
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The software and the databases are developed within the PRETIL platform of the CRIStAL laboratory, which does not allow the diffusion of the software and the data sets at the present time.
References
Al Hage, J., El Najjar, M.E., Pomorski, D.: Multi-sensor fusion approach with fault detection and exclusion based on the kullback-leibler divergence: Application on collaborative multi-robot system. Information Fusion 37, 61–76 (2017)
Angrisano, A., Petovello, M., Pugliano, G.: Benefits of combined gps/glonass with low-cost mems imus for vehicular urban navigation. Sensors 12(4):5134–5158 (2012)
Basseville, M., Nikiforov, IV., et al.: Detection of abrupt changes: theory and application, prentice Hall Englewood Cliffs 104 (1993)
Bertozzi, M., Bombini, L., Broggi, A., Buzzoni, M., Cardarelli, E., Cattani, S., Cerri, P., Debattisti, S., Fedriga, R., Felisa, M., et al.: The vislab intercontinental autonomous challenge: 13,000 km, 3 months, no driver. In: Proc. 17th World Congress on ITS, Busan, South Korea, pp 225–238 (2010)
Borio, D., Dovis, F., Kuusniemi, H., Presti, L.L.: Impact and detection of gnss jammers on consumer grade satellite navigation receivers. Proceedings of the IEEE 104(6):1233–1245 (2016)
Botchkarev, A.: Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology. arXiv preprint (2018). arXiv:1809.03006
Bozorg, M., Nebot, E.M., Durrant-Whyte, H.F.: A decentralised navigation architecture. In: Proceedings. IEEE Int Conf Robot Autom (Cat. No. 98CH36146), 4, pp 3413–3418 (1998)
Carlone, L., Du, J., Ng, M.K., Bona, B., Indri, M.: An application of kullback-leibler divergence to active slam and exploration with particle filters. In: IEEE/RSJ Int Conf Intell Robots Syst , pp 287–293. (2010)
Castellanos, JA., Tardos, JD.: Mobile robot localization and map building: A multisensor fusion approach. Springer Science & Business Media (2012)
Drawil, N.M., Amar, H.M., Basir, O.A.: Gps localization accuracy classification: A context-based approach. IEEE Trans Intell Transp Syst 14(1), 262–273 (2012)
El Faouzi, N.E., Leung, H., Kurian, A.: Data fusion in intelligent transportation systems: Progress and challenges-a survey. Information Fusion 12(1):4–10 (2011)
Endsley, M.R.: Autonomous driving systems: A preliminary naturalistic study of the tesla model s. J Cogn Eng Decis Mak 11(3):225–238 (2017)
Escamilla-Ambrosio, P., Mort, N.: A hybrid kalman filter-fuzzy logic multisensor data fusion architecture with fault tolerant characteristics. In: Proceedings of the 2001 international conference on artificial intelligence, pp 361–367 (2001)
Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp Res A Policy Pract 77, 167–181 (2015)
Fayjie, A.R., Hossain, S., Oualid, D., Lee, DJ.: Driverless car: Autonomous driving using deep reinforcement learning in urban environment. In: 15th International Conference on Ubiquitous Robots (UR), IEEE, pp 896–901. (2018)
Gan, Q., Harris, C.J.: Comparison of two measurement fusion methods for kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic systems 37(1):273–279 (2001)
Georgy, J., Karamat, T., Iqbal, U., Noureldin, A.: Enhanced mems-imu/odometer/gps integration using mixture particle filter. GPS solutions 15(3):239–252 (2011)
Gil, M., Alajaji, F., Linder, T.: Rényi divergence measures for commonly used univariate continuous distributions. Information Sciences 249, 124–131 (2013)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp 315–323 (2011)
Groves, P.D.: Principles of gnss, inertial, and multisensor integrated navigation systems, [book review]. IEEE Aerosp Electron Syst Mag 30(2):26–27 (2015)
Groves, P.D., Jiang, Z.: Height aiding, c/n0 weighting and consistency checking for gnss nlos and multipath mitigation in urban areas. J Navig 66(5):653–669 (2013)
Han, X., Kazim, S.A., Tmazirte, N.A., Marais, J., Lu, D.: Gnss/imu tightly coupled scheme with weighting and fde for rail applications. In: ION ITM Int Tech Meet Inst of Navig, p 14p (2020)
Harmouche, J., Delpha, C., Diallo, D.: Incipient fault detection and diagnosis based on kullback-leibler divergence using principal component analysis: Part i. Signal processing 94, 278–287 (2014)
Harrou, F., Madakyaru, M., Sun, Y.: Improved nonlinear fault detection strategy based on the hellinger distance metric: Plug flow reactor monitoring. Energy and Buildings 143, 149–161 (2017)
Hobza, T., Morales, D., Pardo, L.: Rényi statistics for testing equality of autocorrelation coefficients. Stat Methodol 6(4):424–436 (2009)
Hossain, M.S., Muhammad, G.: Environment classification for urban big data using deep learning. IEEE Commun Mag 56(11):44–50 (2018)
How, J.P., Behihke, B., Frank, A., Dale, D., Vian, J.: Real-time indoor autonomous vehicle test environment. IEEE Control Syst Mag 28(2):51–64 (2008)
Hülsmann, M., Windt, K.: Understanding autonomous cooperation and control in logistics: the impact of autonomy on management, information, communication and material flow. Springer Science & Business Media (2007)
Hwang, I., Kim, S., Kim, Y., Seah, C.E.: A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans Control Syst Technol 18(3):636–653 (2009)
Joerger, M., Pervan, B.: Fault detection and exclusion using solution separation and chi-squared araim. IEEE Trans Aerosp Electron Syst 52(2):726–742 (2016)
Kendoul, F., Fantoni, I., Nonami, K.: Optic flow-based vision system for autonomous 3d localization and control of small aerial vehicles. Robot Auton Syst 57(6–7):591–602 (2009)
Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: A review of the state-of-the-art. Information fusion 14(1):28–44 (2013)
Khoder, M., Nourdine, A.T., Nazih, M., et al.: Fault tolerant multi-sensor data fusion for vehicle localisation using maximum correntropy unscented information filter and \({\alpha }\)-rényi divergence. In: 2020 IEEE 23rd International Conference on Information Fusion (FUSION), pp 1–8 (2020)
Koopman, P., Wagner, M.: Autonomous vehicle safety: An interdisciplinary challenge. IEEE Intell Transp Syst Mag 9(1):90–96 (2017)
Krishnamurthy, A., Kandasamy, K., Poczos, B., Wasserman, L.: Nonparametric estimation of renyi divergence and friends. In: International Conference on Machine Learning, pp 919–927 (2014)
Li, L., Luo, H., Ding, S.X., Yang, Y., Peng, K.: Performance-based fault detection and fault-tolerant control for automatic control systems. Automatica 99, 308–316 (2019)
Makkawi, K., Ait-Tmazirte, N., El Badaoui, El Najjar, M., Moubayed, N.: Adaptive diagnosis for fault tolerant data fusion based on \({\alpha }\)-rényi divergence strategy for vehicle localization. Entropy 23(4):463 (2021)
Novák, A., Havel, K., Bugaj, M.: Measurement of gnss signal interference by a flight laboratory. Transportation research procedia 35, 271–278 (2018)
Ozguner, U., Stiller, C., Redmill, K.: Systems for safety and autonomous behavior in cars: The darpa grand challenge experience. Proceedings of the IEEE 95(2):397–412 (2007)
Petovello, M., O’driscoll, C., Lachapelle, G.: Ultra-tight gps/ins for carrier phase positioning in weak-signal environments. In: Proceedings of NATO RTO SET-104 Symposium on Military Capabilities Enabled by Advances in Navigation Sensors (2007)
Psiaki, M.L., Humphreys, T.E.: Gnss spoofing and detection. Proceedings of the IEEE 104(6), 1258–1270 (2016)
Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Hardt, M., Liu, P.J., Liu, X., Marcus, J., Sun, M., et al.: Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine 1(1), 1–10 (2018)
Schmidt-Hieber, J.: Nonparametric regression using deep neural networks with relu activation function. (2017) arXiv preprint arXiv:1708.06633
Semanjski, S., Semanjski, I., De Wilde, W., Muls, A.: Use of supervised machine learning for gnss signal spoofing detection with validation on real-world meaconing and spoofing data–part i. Sensors 20(4):1171 (2020)
Serdio, F., Lughofer, E., Pichler, K., Buchegger, T., Pichler, M., Efendic, H.: Fault detection in multi-sensor networks based on multivariate time-series models and orthogonal transformations. Information Fusion 20, 272–291 (2014)
Singh, SP., Kumar, A., Darbari, H., Singh, L., Rastogi, A., Jain, S.: Machine translation using deep learning: An overview. In: International conference on computer, communications and electronics (comptelix), IEEE, pp 162–167 (2017)
Srimani, S., Parai, M.K., Ghosh, K., Rahaman, H.: Parametric fault detection of analog circuits based on bhattacharyya measure. Analog Integrated Circuits and Signal Processing 93(3):477–488 (2017)
Strode, P.R., Groves, P.D.: Gnss multipath detection using three-frequency signal-to-noise measurements. GPS solutions 20(3):399–412 (2016)
Suhr, J.K., Jang, J., Min, D., Jung, H.G.: Sensor fusion-based low-cost vehicle localization system for complex urban environments. IEEE Trans Intell Transp Syst 18(5):1078–1086 (2016)
Teoh, E.R., Kidd, D.G.: Rage against the machine? google’s self-driving cars versus human drivers. J Saf Res 63, 57–60 (2017)
Van Brummelen, J., O’Brien, M., Gruyer, D., Najjaran, H.: Autonomous vehicle perception: The technology of today and tomorrow. Trans res part C: emerging technologies 89:384–406 (2018)
Van Erven, T., Harremos, P.: Rényi divergence and kullback-leibler divergence. IEEE Trans Inf Theory 60(7):3797–3820 (2014)
Verdier, G., Hilgert, N., Vila, J.P.: Calcul d’un seuil adaptatif pour des algorithmes de type cusum. In: 37èmes Journées de Statistique, Pau (2005)
Wan, G., Yang, X., Cai, R., Li, H., Zhou, Y., Wang, H., Song, S.: Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. In: IEEE Int Conf Robot Autom (ICRA). pp 4670–4677 (2018)
Wu, Y., Tan, H., Qin, L., Ran, B., Jiang, Z.: A hybrid deep learning based traffic flow prediction method and its understanding. Trans Res Part C Emerg Technol 90, 166–180 (2018)
Xuan, G., Zhu, X., Chai, P., Zhang, Z., Shi, Y.Q., Fu, D.: Feature selection based on the bhattacharyya distance. In: 18th Int Conf Pattern Recog (ICPR’06), IEEE, 4, pp 957–957 (2006)
Youssef, A., Delpha, C., Diallo, D.: An optimal fault detection threshold for early detection using kullback-leibler divergence for unknown distribution data. Signal Processing 120, 266–279 (2016)
Yozevitch, R., Moshe, BB., Weissman, A.: A robust gnss los/nlos signal classifier. NAVIGATION: J Inst Navig 63(4):429–442 (2016)
Zhang, G., Hsu, L.T.: Intelligent gnss/ins integrated navigation system for a commercial uav flight control system. Aerosp Sci Technol 80, 368–380 (2018)
Zhong, J., Liu, Z., Han, Z., Han, Y., Zhang, W.: A cnn-based defect inspection method for catenary split pins in high-speed railway. IEEE Trans Instrum Meas 68(8):2849–2860 (2018)
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Conceptualization, Nesrine Harbaoui, Khoder Makkawi, Nourdine Ait-Tmazirte and Maan El Badaoui El Najjar; Data curation, Nesrine Harbaoui, Khoder Makkawi; Formal analysis, Khoder Makkawi, Nourdine Ait-Tmazirte and Maan El Badaoui El Najjar; Investigation, Nesrine Harbaoui, Khoder Makkawi, Nourdine Ait-Tmazirte and Maan El Badaoui El Najjar; Methodology, Khoder Makkawi, Nourdine Ait-Tmazirte and Maan El Badaoui El Najjar; Supervision, Nesrine Harbaoui, Khoder Makkawi, Nourdine Ait-Tmazirte and Maan El Badaoui El Najjar; Validation, Nesrine Harbaoui, Khoder Makkawi, Nourdine Ait-Tmazirte, Maan El Badaoui El Najjar; Visualization, Nesrine Harbaoui; Writing - original draft, Nesrine Harbaoui; Writing - review and editing, Khoder Makkawi, Nourdine Ait-Tmazirte and Maan El Badaoui El Najjar.
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Harbaoui, N., Makkawi, K., Ait-Tmazirte, N. et al. Context Adaptive Fault Tolerant Multi-sensor fusion: Towards a Fail-Safe Multi Operational Objective Vehicle Localization. J Intell Robot Syst 110, 26 (2024). https://doi.org/10.1007/s10846-023-01906-2
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DOI: https://doi.org/10.1007/s10846-023-01906-2