• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.2
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• pp.138-144
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• 2020

Currently, an autonomous vehicle studies are working to develop a four-level autonomous vehicle that can cope with emergencies. In order to flexibly respond to an emergency, the autonomous vehicle must move in a direction to minimize the damage, which must be conducted by judging all the states of the road, such as the surrounding pedestrians, road conditions, and surrounding vehicle conditions. Therefore, in this paper, we suggest a passenger detection and sharing system to detect the passenger situation inside the autonomous vehicle and share it with V2V to the surrounding vehicles to assist in the operation of the autonomous vehicle. Passenger detection and sharing system improve the weighting method that recognizes passengers in the current vehicle to identify the passenger's position accurately inside the vehicle, and shares the passenger's position of each vehicle with other vehicles around it in case of emergency. So, it can help determine the driving of a vehicle. As a result of the experiment, the body pressure sensor applied to the passenger recognition sub-module showed about 8% higher accuracy than the conventional resonant sensor and about 17% higher than the piezoelectric sensor.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.17-34
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• 2018

The era of fully autonomous vehicles which can travel to their destination completely, is expected to arrive in the near future. The automobile industry will face a huge change in the near future. It makes the industry in the midst of a major turning point, since the automobile was born in the late 1800s. It is expected that the fully autonomous vehicle will be defined as a part of a living space beyond the concept of transportation. However, the existing research on the interior design of the fully autonomous vehicle is insufficient. The purpose of this study is to propose a design method of interior space through analyzing in-vehicle activities, which is a fundamental design element that can satisfy user's emotional experience suitable the concept of living space. For this purpose, the consumer's consciousness about the in-vehicle activity of fully autonomous vehicle was investigated. As a result, domestic consumers perceived the needs of in-vehicle activities related to resting, listening and watching activities such as 'sleeping and resting', 'watching TV and movie', 'listening to music' among 'self - centered' activities. Based on the results of the investigation, this study will suggest a design method of interior design of the fully autonomous vehicle. This study is meaningful, because it is a leading research that suggests new ways of designing interior space by analyzing consumer's needs in a quantitative method.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.448-461
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• 2019

Changes in gravity and buoyancy of a Full Ocean Depth Autonomous Underwater Vehicle (FOD-AUV) during its descending and ascending process must be considered very carefully compared with a Human Occupied Vehicle (HOV) or a Remotely Pperated Vehicle (ROV) whose activities rely on human decision. We firstly designed a two-step weight dropping pattern to achieve a high descending and ascending efficiency and a gravity-buoyancy balance at designed depth. The static equations showed that gravity acceleration, seawater density and displacement are three key aspects affecting the balance. Secondly, we try our best to analysis the gravity and buoyancy changing according to the previous known scientific information, such as anomaly of gravity acceleration, changing of seawater states. Finally, we drew conclusion that gravity changes little (no more than 0.1kgf, it is impossible to give a accurate value). A density-depth relationship at the Challenger Deep was acquired and the displacement changing of the FOD-AUV was calculated preciously.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.4
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• pp.642-651
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• 2016

For the acceleration and brake systems of an autonomous vehicle, the dynamic models from acceleration (brake) pedal input to driving(braking) torque at the vehicle wheel are represented by a set of linear transfer functions in this paper. We present an experimental method that can identify these models using a single rectangular pulse response data. Various magnitude of inputs with different running speeds are applied to experimental tests. All the identified models are demonstrated by the measured data. Both acceleration and brake models have been also validated by comparing the velocity of a full vehicle model associated with the proposed models with the measured vehicle velocity.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.23-31
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• 2023

In recent years, the technology for autonomous driving has been advancing rapidly, ADAS (Advanced Driver Assistance System) functions, which improve driver convenience and safety performance, are mostly equipped in recently released vehicles and range from level 0 to level 2 in autonomous driving technology. Among the various functions of ADAS, AEBS (Autonomous Emergency Braking System), which analyzes traffic accidents, is the most closely related to the vehicle's braking. This study developed a simulation technique for reproducing accidents related to AEBS based on real vehicle experimental data, and it was applied to the analysis of actual ADAS vehicle accidents to identify the causes of accidents.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1113-1114
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• 1996

We describe an algorithm to control an autonomous guided vehicle with 2-diagonal driving wheels, which navigates in the clean room.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.229-231
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• 2018

As autonomous vehicle technology has been gradually developed, robots that have introduced autonomous navigation systems have been actively involved in areas where there is a lot of livelihoods such as industrial sites and accident sites. For this reason, the unmanned transportation system equipped with the autonomous traveling system is widely used in harmful environments where human access is difficult. In addition, the introduction of the autonomous driving system reduces the collision and casualties that occur in a mobility environment like the industrial field, and it helps the efficient work process. In addition, autonomous driving vehicles can be handled more safely and quickly in a wider area by transmitting the surrounding environment of each vehicle to a server connected to each autonomous driving vehicle and passing it through the main server. In this paper, by utilizing V2X communication for autonomous unmanned forklift system, it can increase industrial workload, reduce loss of life and damage to property through wide area forklifts.

• Convergence Security Journal
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• v.19 no.1
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• pp.19-30
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• 2019

As the 4th industrial revolution has recently become a hot topic, the importance of autonomous vehicles has increased and interest has been increasing worldwide, and accidents involving autonomous vehicles have also occurred. With the development of autonomous vehicles, the possibility of a cyber-hacking threat to the car network is increasing. Various countries, including the US, UK and Germany, have developed guidelines to counter cyber-hacking of autonomous vehicles, In the case of Korea, limited temporary operation of autonomous vehicles is being carried out, but the legal system to be applied in case of accidents caused by vehicle network hacking is insufficient. In this paper, based on the existing legal system, we examine the civil liability caused by the cyber hacking of the autonomous driving car, while we propose a law amendment suited to the characteristics of autonomous driving car and a legal system improvement plan that can give sustainable trust to autonomous driving car.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.2
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• pp.128-133
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• 2021

Currently, autonomous vehicle markets are commercializing a third-level autonomous vehicle, but there is a possibility that an accident may occur even during fully autonomous driving due to stability issues. In fact, autonomous vehicles have recorded 81 accidents. This is because, unlike level 3, autonomous vehicles after level 4 have to judge and respond to emergency situations by themselves. Therefore, this paper proposes a vehicle crisis detection system(VCDS) that collects and stores information outside the vehicle through CNN, and uses the stored information and vehicle sensor data to output the crisis situation of the vehicle as a number between 0 and 1. The VCDS consists of two modules. The vehicle external situation collection module collects surrounding vehicle and pedestrian data using a CNN-based neural network model. The vehicle crisis situation determination module detects a crisis situation in the vehicle by using the output of the vehicle external situation collection module and the vehicle internal sensor data. As a result of the experiment, the average operation time of VESCM was 55ms, R-CNN was 74ms, and CNN was 101ms. In particular, R-CNN shows similar computation time to VESCM when the number of pedestrians is small, but it takes more computation time than VESCM as the number of pedestrians increases. On average, VESCM had 25.68% faster computation time than R-CNN and 45.54% faster than CNN, and the accuracy of all three models did not decrease below 80% and showed high accuracy.