cooperative driving; fault and intrusion tolerance; group membership; intersection crossing; location
Abstract :
[en] Cooperation among autonomous vehicles is required whenever efficiency or safety prevents maneuvering based solely on the information of individuals. Intersection crossing is a prominent example of such a situation, where obstructed views create safety concerns and where driving on sight would lead to known inefficient solutions. However, communication, a prerequisite for cooperation, and, in general, the complexity of autonomous driving stacks elevate the threat surface beyond justifiable thresholds, creating the potential for cyberattacks to succeed, particularly when targeting the “brain”. Some of these attacks go undetected and may harm passengers, pedestrians, and other traffic participants in a vehicle’s proximity. In this paper, we address a fundamental challenge of intrusion-resilient maneuver planning: the question of forming consensus groups given variations in the number N of vehicles
that participate in complex maneuvers and given that in a larger group of cars, a larger number F may have already been compromised by an adversary. Introducing confirmed-location-based group membership, we show how trust-anchor-provided precise location information can be leveraged to establish a ground truth about N and F to efficiently solve and agree upon intersection crossing as representative of other complex maneuvers in an F fault-and-intrusion-tolerant manner.
Disciplines :
Computer science
Author, co-author :
RODRIGUES DE MENDONÇA NETO, Júlio ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CritiX
BAYRAMI ASL, Azin ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CritiX
FNR13691843 - Byzrt: Intrusion Resilient Real-time Communication And Computation In Autonomous Systems, 2019 (01/09/2020-31/08/2023) - Marcus Völp FNR15741419 - Resilient And Secure Activity Control For Flexible Time-triggered Systems, 2021 (01/09/2022-31/08/2025) - Marcus Völp
Funders :
FNR - Fonds National de la Recherche DFG - Deutsche Forschungsgemeinschaft
Funding number :
C19-IS-13691843; C21/IS/15741419
Funding text :
This work has been supported by the Luxembourg Fond Nationale de Recherche (FNR) and the German Research Council (DFG) through the CORE Inter Projects ByzRT (C19-IS-13691843) and ReSAC (C21/IS/15741419).
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