Cognitive Load Analysis of Cybersecurity Interfaces in Autonomous Vehicle Control Systems

Abstract

To assure the functioning of such semi-autonomous systems, it is vital for the operator to cooperate efficiently with both the vehicular human-machine interface (HMI) and the security systems. The driver’s workload can increase due to both the manual control of the vehicle during the levels of low driving automation and the safety critical control to regain the manual driving of the vehicle from conditional and high driving-level automation based on mainly cyber and physical events systems failure. We shall delve into deep-dive details and outline analysis of the effects of the choices regarding the logic and HMI design of the cybertrail on the operation of the autonomous vehicles. The art-of-the-state interfaces to allow the operator to provide the Legal Request for The Control (LRfC) to gain the manual control during the cyber event and HMI designs to provide security aspects’ logic are vital concerning the sustainability of autonomous vehicles. Such UIs also describe the cyber events or follow the various warning levels and possess separate logic units to decrease the cognitive load of the operators and thereby make it easier for interest to achieve satisfactory security status for traffic [1].