The Effects of Vehicle Automation Level and Warning Type on Responses to Vehicle Hacking
Modern surface transportation vehicles often include different levels of automation. Higher automation levels have the potential to impact surface transportation in unforeseen ways. For example, connected vehicles with higher levels of automation are at a higher risk for hacking attempts, because automated driving assistance systems often rely on on board sensors and internet connectivity. As the automation level of vehicle control rises, it is necessary to examine the effect of different levels of automation have on the driver-vehicle interactions. In addition, auditory warnings have been shown to effectively attract a driver’s attention while performing a driving task, which is often visually demanding. The purpose of the current study was to examine the effect of level of automation and warning type on the driver’s responses to vehicle hacking attempts. This goal was accomplished by manipulating level of automation (manual vs. automated) and warning type (non-semantic vs. semantic) and measuring drivers’ responses to a vehicle hacking attempt using time to collision (TTC) values, maximum steering wheel angle, number of successful responses, and other measures of response. Our results revealed no significant effect of level of automation or warning type on TTC or successful response rate. However, there was a significant effect of level of automation on maximum steering wheel angle such that manual drivers had safer responses to the hacking attempt with smaller maximum steering wheel angles. In addition, an effect of warning type that approached significance was also found for maximum steering wheel angle such that participants who received a semantic warning had more severe and dangerous responses to the hacking attempt. The current results suggest that level of automation and warning type may not significantly affect how quickly people respond to hacking scenarios when the warning is given in advance, but they may affect the quality of the driver’s response. These findings are important to future vehicle system design and subsequently the safety of future roadways.
Automation, Driving, Level of automation, Situation awareness, Vehicle hacking, Warning, Applied Behavior Analysis, Human Factors Psychology, Transportation
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