An Analysis of Driver-Initiated Takeovers during Assisted Driving and their Effect on Driver Satisfaction

TL;DR

This study analyzes driver-initiated takeovers during assisted driving, revealing key reasons behind them and their negative impact on driver satisfaction, highlighting the importance of personalized ADAS behavior adaptation.

Contribution

It provides a detailed categorization of takeover reasons and demonstrates how adapting ADAS to driver preferences can improve satisfaction and reduce takeovers.

Findings

Takeovers mainly aim to adjust ADAS behavior within its ODD.

Frequent takeovers within ODD negatively affect driver satisfaction.

Driver takeover behavior varies significantly among individuals.

Abstract

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.