Confirmed-Location Group Membership for Intrusion-Resilient Cooperative Maneuvers

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.