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Resilience by design & failures forecasting for a connected autonomous vehicle

Abstract : Autonomous vehicles with an automation level 5 will drive autonomously in any road scenarios such as highways, snowy roads, urban areas, or traffic jams. The integration of V2X communication, as a new source of perception for the vehicle could remove the limitations of local perception by communicating with an occluded pedestrian or by detecting in advance the presence of a vehicle under a heavy mist. However, this V2X communication may be a new source of attacks threatening the vehicle perception. Current countermeasures are not designed for all autonomous vehicles because these countermeasures require the driver assistance or work with a specific set of sensors. Therefore, the thesis aims to propose a generic failure resilient perception architecture for all types of connected and autonomous vehicles supporting different kinds of sensors. In this thesis, we propose a generic perception architecture named GPA with its failure resilient perception algorithm (FRPA). We propose a new threat analysis and risk assessment method named SARA that identifies and assess the risk of attacks targeting connected and automated vehicles with an automation level 5. To identify where and how these attacks occur, we propose an attacker and a security goal model for all automotive perception systems. We implemented two modules of our failures resilient perception algorithm (FRPA): a Machine Learning based Failure Classifier and a V2X-Sensor Correlation Module considering three kinds of source: camera, radar, and V2X. We highlighted several new attacks in the perception pipeline and raise the need for new security countermeasures such as the physical integrity of road infrastructures and trustworthy perception algorithms. Besides, our countermeasures based on machine learning and sensor correlation showed very accurate results to detect and classifies perception failures (over 90% accuracy score). Finally, the ideas developed in the thesis resulted in 10 filled patents and several publications.
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Submitted on : Friday, March 20, 2020 - 3:50:09 PM
Last modification on : Wednesday, January 26, 2022 - 2:31:29 PM
Long-term archiving on: : Sunday, June 21, 2020 - 4:12:00 PM


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  • HAL Id : tel-02513392, version 1


Jean-Philippe Monteuuis. Resilience by design & failures forecasting for a connected autonomous vehicle. Networking and Internet Architecture [cs.NI]. Institut Polytechnique de Paris, 2020. English. ⟨NNT : 2020IPPAT003⟩. ⟨tel-02513392⟩



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