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.

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“…However, no significant differences were observed between the three groups as far as the time to collision is concerned. Furthermore, according to the age group, no significant differences were observed regarding the reaction time nor the maximum lateral position; this result is in agreement with Korber et al [23] who found that older drivers handle critical traffic events and adapt to the experience of take-over situations as well as younger drivers. Finally, considering the TOR caused by loss of road marking, the stability of the trajectory in the 30 seconds after the TOR was evaluated and no significant differences between the groups were observed comparing the maximum lateral distance from the center of the lane.…”

“…To study driver's distraction during automated driving, researchers generally use standardized and naturalistic tasks. Standardized tasks (such as the cognitive n-back task [21], the SuRT task 140 [21,22], the Twenty Questions Task (TQT) [23]) provide experimental control, but they do not usually correspond to what the driver will do in the vehicle. Naturalistic tasks, instead, provide ecological validity, but they could introduce experimental bias.…”

“…Most of the studies in this domain implement safetycritical take-over scenarios caused by an obstacle (usually a 155 broken down vehicle) on the current lane [24,17,21,25,23] and non-critical scenarios caused by the absence of lane markings [24,26]. To ensure security and to succeed in the take-over process, it is important to understand how much time before a system boundary a driver who is out of the loop should be warned.…”

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

“…Furthermore, conditionally automated vehicles need to allow for a "sufficiently comfortable transition time" [12] of "several seconds" after presenting a TOR [5]. In an attempt to gain an understanding of how long drivers need to resume control from an automated vehicle, Eriksson and Stanton [13] reviewed the literature on control transitions and found that drivers take a median of 2.5 s, and in some cases up to 15 s to resume control in urgent scenarios (e.g., [14]). Their review also showed that when drivers are requested to resume control without time pressure, they take between 2.1 and 3.5 s (median) longer than when under time pressure, depending on task engagement [13].…”

Automated Vehicle Handover Interface Design: Focus Groups with Learner, Intermediate and Advanced Drivers

“…Arguably, with age come changes in the way the driving task is performed, and studies looking into driver skill are inevitably confounded by this variable, a factor recognized across driving research [37]. In controlled experiments exploring takeover performance and age, Körber et al [38] found little differences in behavioral performance when drivers were asked to take control with and without a secondary task. Morgan et al [8] argued that most handover studies focused on mainly middle-aged and high-mileage drivers.…”