The influence of age on the take-over of vehicle control in highly automated driving

Abstract

The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (≥ 60 years) compared to younger drivers (≤ 28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.

Examining the Effects of Emotional Valence and Arousal on Takeover Performance in Conditionally Automated Driving

“…Our results showed that this process took less than 2 seconds, no matter in which emotional state drivers were. Recent studies comparing different types of NDRTs on takeover quality and timeliness showed that the types of NDRTs only influenced the takeover quality and not takeover time (Bueno et al, 2016;Gold et al, 2016;Körber et al, 2016;Zeeb et al, 2016;Zeeb, Härtel, Buchner, & Schrauf, 2017), providing further support for our findings.…”

Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars

“…Drivers are misusing those systems for example by leaving the driver's seat while driving on public roads using a level 2 driving automation system (Krok 2015). Considering how difficult it is for the driver to get back in the loop and properly react to certain traffic situations (Gold et al 2013;Körber et al 2016), such reports are troubling. They show that exaggerated benefit perceptions can have negative implications for driving safety and, thus, public acceptance.…”

Introduction matters: Manipulating trust in automation and reliance in automated driving

“…Indeed, this reflects the conceptualization of trust as a mainly affective response with influences by analytic and analogical processes. Since the future driving automation users will not be a homogenous group of trained experts, potential variability in driver characteristics such as trust can lead to very different outcomes, especially in time-critical situations like take-over situations (Creaser & Fitch, 2015;Körber, Gold, Lechner, & Bengler, 2016). For example, in June 2016, the first fatal accident caused by a self-driving car occurred.…”