An Analysis of Driver-Initiated Takeovers during Assisted Driving and their Effect on Driver Satisfaction
CoRR(2024)
摘要
During the use of Advanced Driver Assistance Systems (ADAS), drivers can
intervene in the active function and take back control due to various reasons.
However, the specific reasons for driver-initiated takeovers in naturalistic
driving are still not well understood. In order to get more information on the
reasons behind these takeovers, a test group study was conducted. There, 17
participants used a predictive longitudinal driving function for their daily
commutes and annotated the reasons for their takeovers during active function
use. In this paper, the recorded takeovers are analyzed and the different
reasons for them are highlighted. The results show that the reasons can be
divided into three main categories. The most common category consists of
takeovers which aim to adjust the behavior of the ADAS within its Operational
Design Domain (ODD) in order to better match the drivers' personal preferences.
Other reasons include takeovers due to leaving the ADAS's ODD and corrections
of incorrect sensing state information. Using the questionnaire results of the
test group study, it was found that the number and frequency of takeovers
especially within the ADAS's ODD have a significant negative impact on driver
satisfaction. Therefore, the driver satisfaction with the ADAS could be
increased by adapting its behavior to the drivers' wishes and thereby lowering
the number of takeovers within the ODD. The information contained in the
takeover behavior of the drivers could be used as feedback for the ADAS.
Finally, it is shown that there are considerable differences in the takeover
behavior of different drivers, which shows a need for ADAS individualization.
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关键词
Driver Satisfaction,Differences In Behavior,Personal Preferences,Take Back,Driver Behavior,Advanced Driver Assistance Systems,Back Control,Take Back Control,Type Of Treatment,Participant Data,Number Of Treatments,Global Positioning System,Treatment Frequency,Inertial Measurement Unit,Speed Limit,Agglomerative Clustering,Map Information,Annotated Dataset,Optimal Order,Speed Of Adjustment,Data Bus,Target Vehicle,Straight Road,Label Distribution,Number Of Annotations,Roundabout,Individual Drivers,Drivers Of Preferences,High Speed,Traffic Light
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