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Satisfaction of Modeling Requirements for Intelligent Navigation Systems: Risk Management Context

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Informatics in Control, Automation and Robotics (ICINCO 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 793))

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Abstract

This work aims to boost the reliability and safety of Intelligent Transportation Systems (ITSs). To meet this goal, a particular risk assessment and management scheme is introduced to provide navigation approaches with strong safety guarantees. On the one hand, the interval analysis is adopted to develop a high fidelity model used for risk assessment purposes. This model is based on a set-membership computation of the Time-To-Collision (TTC) indicator. The TTC approximation methodology, which fits well the car-following scenario, takes into account several uncertainties of distinct sources. Even more, a novel second-order set-membership TTC formalization is introduced by solving a polynomial equation with interval coefficients. This formalization is suggested in an effort to diminish modeling errors. Both the first and second order interval-based TTC are improved via a correlation analysis-based statistical process. On the other hand, the complexity aspects of modern architectures are analyzed in this work. The tackled analysis emphasizes the destructive impacts of the inter/intra-vehicular communication on ITSs reliability. Thus, a Response Time Analysis (RTA) scheme is integrated into the proposed risk management to consider explicitly the communication latency-related material constraints. Then, the RTA results are involved into the simulation work. Finally, the simulation results applied on an adaptive cruise control system of both high/low-order TTC formalizations demonstrate that the low-order model inaccuracy is compensated. Through the interval/correlation analysis and the consideration of the material constraints, a great balance between modeling accuracy and simplicity is performed.

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Acknowledgements

The present work is supported by the WOW (Wide Open to the World) program of the CAP 20-25 project. It receives also the support of IMobS3 Laboratory of Excellence (ANR-10-LABX-16-01).

Author information

Authors and Affiliations

  1. Institut Pascal, UCA/SIGMA-UMR CNRS 6602, Clermont Auvergne University, Clermont-Ferrand, France

    Nadhir Mansour Ben Lakhal

  2. LATIS Lab, National Engineering School of Sousse (ENISo), University of Sousse, BP 264, 1023, Sousse Erriadh, Tunisia

    Nadhir Mansour Ben Lakhal, Othman Nasri & Jaleleddine Ben Hadj Slama

  3. Heudiasyc UMR CNRS/UTC 7253, Université de Technologie de Compiègne, 60203, Compiègne, France

    Lounis Adouane

Authors
  1. Nadhir Mansour Ben Lakhal
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  2. Othman Nasri
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  3. Lounis Adouane
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  4. Jaleleddine Ben Hadj Slama
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Corresponding author

Correspondence to Nadhir Mansour Ben Lakhal .

Editor information

Editors and Affiliations

  1. Ford Research and Advanced Engineering, Dearborn, MI, USA

    Oleg Gusikhin

  2. University Paris-Est Créteil (UPEC), Créteil, France

    Kurosh Madani

  3. University of Reims Champagne-Ardenne, Reims, France

    Janan Zaytoon

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Ben Lakhal, N.M., Nasri, O., Adouane, L., Ben Hadj Slama, J. (2022). Satisfaction of Modeling Requirements for Intelligent Navigation Systems: Risk Management Context. In: Gusikhin, O., Madani, K., Zaytoon, J. (eds) Informatics in Control, Automation and Robotics. ICINCO 2020. Lecture Notes in Electrical Engineering, vol 793. Springer, Cham. https://doi.org/10.1007/978-3-030-92442-3_23

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