Abstract
The interaction between the driver and the automated driving systems (ADS) will remain a key element of automated driving because drivers are expected to be available to take over control for the case of system failure or limitation in a conditionally automated vehicle, i.e. SAE level 3. A number of studies reported that various factors such as the time budget, the traffic complexity, and the driver’s inattention may influence take-over time and quality. Therefore, the driver’s take-over performance must be carefully analyzed to ensure a safe transition. This study aims to propose a take-over performance assessment method using a driving simulator. A systematic review was conducted to design a driving scenario for unintended take-over events. As a result, a take-over performance test protocol with four take-over situations such as missing lines on a straight and a curved road, road construction, and system failure was designed. Visual and cognitive non-driving related tasks, which influence a driver’s situation awareness and take-over performance, were also considered. It will be proposed as Korean Traffic Safety Regulation to assess the safety of the take-over control in a conditionally automated vehicle from the perspective of the driver.
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1 Introduction
Improvement of the automation level frees drivers from the primary driving tasks and allows to perform non-driving related tasks by shifting attentional resources to other tasks during driving [1]. However, the existing automated driving systems (ADS) are still considering a driver as a fallback-ready user who is receptive to the ADS-issued requests to take-over [1, 2]. Previous studies on automation and human factors found that a high level of automation can cause out-of-the-loop problems [3] and humans are not good at tasks that require vigilance for prolonged periods of time [4]. Thus, the driver’s take-over performance must be carefully investigated to ensure a safe transition in a conditionally automated vehicle. This paper aims to propose a harmonized experimental protocol to assess drivers’ take-over performance in a driving simulator and compare the results around the world.
2 Research Frameworks
In order to design an experimental protocol, we adopted a framework for human factors of transitions in automated driving [1, 5] and categorized the factors for the simulator-based take-over experiment design (Table 1). The factors are assigned to four simulation design elements such as participants, driving contexts, control contexts and ADS design. Among the simulation design elements, the participants and the ADS design were not considered because the recruitment of participants is dependent on the size and budget of the experiment and the ADS design are related to the individual design philosophy. The driving contexts was further investigated to propose a harmonized experimental scenario and summarized in Table 2. Except the system failure, most of the driving context factors were used in the previous studies. Although the system failure may affect the participant’s perceived reliability and safety, it is worth considering as a safety-critical event.
3 Proposed Experimental Protocol
3.1 Experimental Design
In this study, the factors including ‘take-over events’, ‘traffic density’ and ‘NDRT’ were selected as independent variables. They are expected to affect situation awareness and drivers’ readiness. As mentioned in the previous section, age, gender, and experience are considered as latent variables that can influence the take-over quality and take-over performance. Therefore, it is worth to note to recruit participants considering the proportion of sample size.
3.2 Take-over Situations in Driving Contexts
Based on the literature survey of the previous studies, this study proposes the driving contexts of four representative situations such as “Lane marking is missing on a straight road (S1)”, “Lane marking is missing on a curved road (S2)”, “Roadwork appears during driving a straight road (S3)”, and “An automated system is deactivated due to system failure on a straight road (S4)”. As shown in Table 3, S1 is the scenario with the lowest complexity and S4 is the highest.
3.3 Take-over Performance Measures
Reaction time, take-over time and take-over quality measures are typical dependent variables in take-over related studies. The potential measures to assess take-over performance are summarized in Table 4.
4 Summary and Concluding Remarks
This paper proposed a simulator-based method to evaluate the take-over performance of the conditionally automated vehicle. In this study, we categorized a driving simulator experimental protocol into four components, i.e., driving contexts, control contexts, ADS design and participants. Then the driving context related parameters were selected based on the previous studies. Finally, four take-over events and performance measures were proposed. The result of this study may contribute to establish a guideline for take-over experiments. A pilot test will be conducted based on this experimental design.
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Acknowledgements
This work was supported in part by a grant (code 18TLRP-B131486-02) from Transportation and Logistics R&D Program funded by Ministry of Land, Infrastructure and Transport of Korean government, and by DGIST R&D Program (18-BT-01) of the Ministry of Science and ICT of Korean government.
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Son, J. et al. (2018). A Simulator-Based Approach to Assess Take-over Performance in a Conditionally Automated Vehicle. In: Stephanidis, C. (eds) HCI International 2018 – Posters' Extended Abstracts. HCI 2018. Communications in Computer and Information Science, vol 852. Springer, Cham. https://doi.org/10.1007/978-3-319-92285-0_54
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