• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.167-189
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• 2022

In the case of SAE autonomous driving levels 2 and 3, since complete autonomous driving is impossible, the take-over process is essential, and take-over time(TOT) is the most important factor in determining the safety of the autonomous driving system. Accordingly, research on TOT is being actively conducted, but each research is independently conducted and general conclusions that integrate various research results are required. Therefore, in this study, the factors affecting TOT were analyzed using meta-analysis, which integrates the results of individual studies and presents an integrated opinion. As a result of meta-analysis, a total of 10 influencing factors were selected, and most of them were related to the non-driving related task(NDRT) type. In addition, implications for the future research direction of take-over and NDRT were presented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.31-43
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• 2019

Take-overs occur in autonomous vehicles at levels 3 and 4 based on SAE. For safe take-over, it is necessary to set the time required for diverse drivers to complete take-over in various road conditions. In this study, take-over time and stabilization characteristics were measured to secure safety of take-over in autonomous vehicle. To this end, a virtual driving simulator was used to set up situations similar to those on real expressways. Fifty drivers with various sexes and ages participated in the experiment where changes in traffic volume and geometry were applied to measure change in takeover time and stabilization characteristics according to various road conditions. Experimental results show that the average take-over time was 2.3 seconds and the standard deviation was 0.1 second. As a result of analysis of stabilization characteristics, there was no difference in take-over stabilization time due to the difference of traffic volume, and there was a significant difference by curvature changes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.246-263
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• 2023

The stabilization time of take-over refers to the time it takes for driving to stabilize after the take-over. Following a take-over request from an automated driving system, the driver must become aware of the road driving environment and perform manual driving, making it crucial to clearly understand the relationship between the driving environment and stabilization time of take-over. However, previous studies specifically focusing on stabilization time after take-over are rare, and research considering the driving environment is also lacking. To address this, our study conducted experiments using a driving simulator to observe take-over transitions. The results were analyzed using a liner mixed model to quantitatively identify the driving environment factors affecting the stabilization time of take-over. Additionally, coefficients for stabilization time based on each influencing factor were derived.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.70-82
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• 2022

At the current stage of a semi-autonomous vehicle, there are situations in which the vehicle has to request take-over control to the driver quickly. However, current self-driving cars use only simple messages and warning sounds to notify drivers when handing over control, so they do not adequately convey considerations of individual characteristics or explanations of various emergent situations. This study investigated how visual and auditory information and the efficacy of drivers in self-driving cars can improve efficient take-over requests between the car and the driver. We found that there were significant differences in driver's cognitive load, reliability, safety, usability, and usefulness according to the combination of three visual and auditory information provided in the experiment of the take-over request situation. The results of this study are expected to help design self-driving vehicles that can communicate more safely and efficiently with drivers in urgent control transition situations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.142-151
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• 2018

In the era of the 4th Industrial Revolution, research and development on autonomous vehicles have been actively conducted all over the world. Under these international trends, the Ministry of Land, Infrastructure and Transport is actively promoting the development of autonomous vehicles aiming at commercialization of autonomous vehicles at level 3 or higher by 2020. In the level 3 autonomous vehicle, it is essential to transfer control between the driver and the vehicle according to driving situations. Prior to the full-fledged autonomous vehicle age, this study developed a representative scenario for the safety evaluation on take-over on expressways. To accomplish this, we developed a highway driving scenario first, and then developed six control transition scenarios based on 2014 highway traffic accident data and take-over data. The variables to be considered in the developed scenarios are divided into drivers, vehicles, and environmental factors. A total of 36 variables are selected.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.101-111
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• 2022

Conditional autonomous vehicles should hand over control to the driver according on driving situations. However, if the driver is immersed in a non-driving task, the driver may not be able to make suitable decisions. Previous studies have confirmed that the cues enhance take-over performance with a directional information on driving. However, studies on the effect of take-over cues on the driver's brain activities are rigorously investigated yet. Therefore, this study we evaluates the driver's brain activity according to the take-over cue. A total of 25 participants evaluated the take-over performance using a driving simulator. Brain activity was evaluated by functional near-infrared spectroscopy, which measures brain activity through changes in oxidized hemoglobin concentration in the blood. It evaluates the activation of the prefrontal cortex (PFC) in the brain region. As a result, it was confirmed that the driver's PFC was activated in the presence of the cue so that the driver could stably control the vehicle. Since this study results confirmed that the effect of the cue on the driver's brain activity, and it is expected to contribute to the study of take-over performance on biomakers in conditional autonomous driving in future.

• Journal of the HCI Society of Korea
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• v.14 no.2
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• pp.21-29
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• 2019

Take-over requests in semi-autonomous cars must be handled properly in the case of road obstacles or curved roads in order to avoid accidents. In these situations, situation awareness and appropriate decision making are essential for distracted drivers. This study used a driving simulator to investigate the components of auditory-visual information systems that affect safety, workload, and trust. Auditory information consisted of either voice guidance providing situation awareness for the take-over or a beep sound that only alerted the driver. Visual information consisted of either a screen showing how to maneuver the vehicle or only an icon indicating a take-over situation. By providing auditory information that increased situation awareness and visual information that aided decision making, trust and safety increased, while workload decreased. These results suggest that the levels of situation awareness and decision making ability affect trust, safety, and workload for drivers.

• Design Convergence Study
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• v.17 no.4
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• pp.17-29
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• 2018

In the semi-autonomous vehicle, before reaching a fully autonomous driving stage, it is imperative for the system to issue a take-over request(TOR) that asks a driver to operate manually in a specific situation. The purpose of this study is to compare whether head-up display(HUD) is a better human-vehicle interaction than head-down display(HUD) in the event of TOR. Upon recognition of TOR in the experiment with a driving simulator, participants were prompted to switch over to manual driving after performing a secondart task, that is, playing a game, while in auto-driving mode. The results show that HUD is superior to HDD in 'ease of use' and 'satisfaction' although there is no significant difference in reaction time and subjective workload. Therefore, designing secondary tasks through HUD during autonomous driving situation improves the user experience of the TOR function. The implication of this study lies in the establishing an empirical case for setting up UX design guidelines for autonomous driving context.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.235-249
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• 2020

The Ministry of Land, Infrastructure and Transport established safety standards for Level 3 autonomous vehicles for the first time in the world in December 2019, and specified the safety standards for conditional autonomous driving systems. Accordingly, it is necessary to analyze the influence of various driving environments on take-over. In this study, using a driving simulator, we investigated how traffic conditions and weather conditions affect take-over time and stabilization time. The experimental procedure was conducted in the order of preliminary training, practice driving, and test driving, and the test driving was conducted by dividing into a traffic density and geometry experiment and a weather environment experiment. As a result of the experiment, it was analyzed that the traffic volume and weather environment did not affect the take-over time and take-over stabilization time, and only the curve radius affects take-over stabilization time.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.113-122
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• 2018

In level 3 automated vehicles, drivers are expected to encounter transition of control when the system reaches its limit. Drivers need to refocus their attention on the road ahead and gain situational awareness. Appropriate information should be conveyed during this period in order to prevent human errors. In this paper, we defined the takeover process as 'in-the-middle-of-the-loop' and conducted Task Analysis and Work Domain Analysis to find out information requirements. As a result, we specified required information types and interface considerations. Moreover, we conducted an experimental study to find how the information types affect drivers on situation awareness, cognitive load and reaction time. Consequently, we found different information on system transparency should be conveyed depending on the urgency of takeover situation and driver guidance could help drivers with better situation awareness after the takeover.