How to Cite
Abstract
In this paper, we describe a novel approach to the application and quantification of cognitive load to an autonomous vehicle threat detection and classification learning environment. The work uses an increased number of sensors to allow a more accurate representation of an autonomous vehicle's environment that in turn creates a more comprehensive view of the training environment, increasing operator awareness and reducing the operator's cognitive workload. We describe a learning pathway method using students as participants that functionally involves developing and testing their awareness of an autonomous vehicle's environment under different levels and types of external attack. The levels of attack can be combined and increased to represent a complex prioritized system. The preliminary analysis and the results of the student-pathway-environment models are described for an initial baseline case.
There is a prevailing view that machine learning (ML) and artificial intelligence (AI) will be the panacea to the challenges of autonomous vehicles in cybersecurity. While this is a significant part of the solution, machine learning is ultimately a tool that is employed to enhance a system's abilities in a specific domain. Such techniques rely on comprehensive and relevant data in that area to begin with and be applied thoughtfully if they are to be effective. A fundamental solution to these problems is to improve the experience of the autonomous vehicle operators. An improved understanding of operator cognitive load can allow the maximum benefit to be drawn from machine learning, as well as ensuring operators are better equipped to spot and rectify emergent problems.
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