• 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.

• ETRI Journal
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• v.45 no.1
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• pp.75-92
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• 2023

Level 3 autonomous vehicles require conditional autonomous driving in which autonomous and manual driving are alternately performed; whether the driver can resume manual driving within a limited time should be examined. This study investigates whether the demographics and subjective driving tendencies of drivers affect the take-over performance. We measured and analyzed the reengagement and stabilization time after a take-over request from the autonomous driving system to manual driving using a vehicle simulator that supports the driver's take-over mechanism. We discovered that the driver's reengagement and stabilization time correlated with the speeding and wild driving tendency as well as driving workload questionnaires. To verify the efficiency of subjective questionnaire information, we tested whether the driver with slow or fast reengagement and stabilization time can be detected based on machine learning techniques and obtained results. We expect to apply these results to training programs for autonomous vehicles' users and personalized human-vehicle interfaces for future autonomous vehicles.

• 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.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.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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.3
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• pp.307-316
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• 2016

A multiple radar system is comprised of early warning radar for fast detection of a target and air defense radar for precision intercept. For this reason, target take-over process is required between the two radars. The target take-over should be performed at an appropriate time by consideration of stable tracking and effective fire control. In this paper, operation characteristics of multiple radar system are analyzed and target take-over time determination method using estimation of target tracking performance is proposed for high-velocity targets. The proposed method is validated with ballistic target defense scenarios in the developed integrated simulator.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.70-76
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• 2022

This paper investigates the effects of age, gender, and situational factors on take-over performance in automated driving. The existing automated driving systems still consider a driver as a fallback-ready user who is receptive to take-over requests. Thus, we need to understand the impact of situations and human factors on take-over performance. 34 drivers drove on a simulated track, consisting of one baseline and four event scenarios. The data, including the brake reaction time and the standard deviation of lane position, and physiological data, including the heart rate and skin conductance, were collected. The analysis was performed using repeated-measures ANOVA. The results showed that there were significant age, gender, and situational differences in the takeover performance and mental workload. Findings from this study indicated that older drivers may face risks due to their degraded driving performance, and female drivers may have a negative experience on automated driving.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.75
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• pp.1-28
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• 2017

Article 67 of CISG which provides for the passing of risk of loss of the goods applies to the contract of sale involving carriage of the goods. The risk here is in nature the price risk. Under Article 67(1), if the seller is bound to hand the goods over to a carrier at a particular place, the risk passes to the buyer when the goods are handed over to the carrier at that place; if the seller is not bound to hand them over at a particular place, the risk passes to the buyer when the goods are handed over to the carrier. In these cases, the risk passes even though the seller duly retains documents controlling the disposition of the goods. Article 69 of CISG applies to the contract of sale that does not involve carriage of the goods. Under Article 69(1) which covers the situation that the buyer is bound to take over the goods at the place of business of the seller, the risk passes when the buyer takes over the goods, however if the buyer does not take over the goods in due time, the risk passes at the time when the goods are placed at the buyer's disposal and he commits a breach of contract by failing to take delivery. Under Article 69(2) which covers the situation that the buyer is bound to take over the goods at a place (including his own place of business) other than the place of business of the seller, the risk passes when delivery is due and the buyer is aware of the fact that the goods are placed at his disposal at that place. Under these provisions of CISG, this study suggests what should be the definition of the contract of sale involving carriage of the goods. This study goes further to looks into what should be the concepts of the handing over of the goods by the seller to the carrier, the taking over of the goods by the buyer and the placing the goods at the buyer's disposal by the seller. This study may, we hope, provide a guidance for clearer understanding of the exact time of passing of risk under CISG.

• Journal of Auto-vehicle Safety Association
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• v.12 no.1
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• pp.15-25
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• 2020

This paper presents a longitudinal control algorithm for ensuring takeover time of autonomous vehicle using V2V communication. In the autonomous driving of more than level 3, autonomous systems should control the vehicles by itself partially. However if the driver's intervention is required for functional safety, the driver should take over the control reasonably. Autonomous driving system has to be designed so that drivers can take over the control from autonomous vehicle reasonably for driving safety. In this study, control algorithm considering takeover time has been developed based on computation method of takeover time. Takeover time is analysed by conditions of longitudinal velocity of preceding vehicle in time-velocity plane. In addition, desired clearance is derived based on takeover time. The performance evaluation of the proposed algorithm in this study was conducted using 3D vehicle model with actual driving data in Matlab/Simulink environment. The results of the performance evaluation show that the longitudinal control algorithm can control while securing takeover time reasonably.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2006.11a
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• pp.212-215
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• 2006

The Beautiful rest room is simple how to think. We need to understand that excretion is important as much as eating. When we didn't have foods, eating was important in itself no matter what our lives environments. But now we want to eat in more dignified and more cultural place. Most of restaurants spend money on adjusting place for the consumes' desire. It is same way about rest room, it is time to change our rest room to place of rest and speculation over the place which we fix our natural urge. We can fix our natural urge, take a rest, and get creative ideas in more convenient, more healthy and more comfortable. It is time for us to change out thought about rest room; it is not dirty and stink place, but civilized living place like developed country. Also, we need new concept to take a triangular position about rest room is one of structure over just decorations. This research suggest new public rest room without precedent based on instance analysis about rest room inside and outside the country.