Driver response to take-over requests in real traffic
Journal article, 2023

Existing research on control-transitions from automated
driving (AD) to manual driving mainly stems from studies
in virtual settings. There is a need for studies conducted in real
settings to better understand the impacts of increasing vehicle
automation on traffic safety. This study aims specifically to understand
how drivers respond to take-over requests (TORs) in real
traffic by investigating the associations between 1) where drivers
look when receiving the TOR, 2) repeated exposure to TORs, and
3) the drivers’ response process. In total, thirty participants were
exposed to four TORs after about 5–6 min of driving with AD on
public roads. While in AD, participants could choose to engage in
non-driving-related tasks (NDRTs).When they received the TOR,
for 38% of TORs, participants were already looking on path. For
those TORs where drivers looked off path at the time of the TOR,
the off-path glance was most commonly towards an NDRT item.
Then, for 72% of TORs (independent on gaze direction), drivers
started their response process to the TOR by looking towards
the instrument cluster before placing their hands on the steering
wheel and their foot on the accelerator pedal, and deactivating
automation. Both timing and order of these actions varied among
participants, but all participants deactivated AD within 10 s from
the TOR. The drivers’ gaze direction at the TOR had a stronger
association with the response process than the repeated exposure
to TORs did. Drivers can respond to TORs in real traffic. However,
the response should be considered as a sequence of actions that
requires a certain amount of time.

take-over request.

driver behavior

driving performance

driver response

automation

Automated driving

Author

Linda Pipkorn

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Emma Tivesten

Volvo Cars

Carol Ann Cook Flannagan

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

Marco Dozza

Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety

IEEE Transactions on Human-Machine Systems

2168-2291 (ISSN) 21682305 (eISSN)

Vol. 53 5 823-833

Subject Categories

Infrastructure Engineering

Applied Psychology

Human Computer Interaction

Vehicle Engineering

DOI

10.1109/THMS.2023.3304003

More information

Latest update

10/20/2023