• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.13-24
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• 2021

In level 5 (mind-off) of autonomous driving, the autonomous vehicle passengers are expected to have various activities such as face-to-face meetings, working, relaxing, and watching movies. In particular, various changes in the interior space of the vehicle are expected. Moreover, according to the survey conducted by the American Automobile Association, 73% of the respondents reported that they were afraid to board autonomous vehicles. In level 5 of autonomous driving, the subject of safety was expected to be transferred to autonomous vehicles; thus, research should be conducted from the user's perspective. Recently, various studies have been conducted to secure the safety of fully autonomous vehicles. However, there are limited studies addressing the psychological safety of actual passengers. Therefore, this study conducted a questionnaire based on the AHP technique. Consequently, the automobile safety system's priority for securing passengers' psychological safety according to each type of indoor behavior was derived, and the interior space for securing the psychological stability of passengers was suggested based on the obtained results. This study offers a new direction for interior space design, satisfying the psychological safety of passengers. This study is important because it advocates that the interior environment of fully autonomous driving cars is expected to be designed to secure the user's psychological safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.6
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• pp.299-310
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• 2022

The ability of an automated driving system is based on vehicle sensors, judgment and control algorithms, etc. The safety of automated driving system is highly related to the operational status of the road network and compliant road infrastructure. The safe operation of automated driving necessitates continuous monitoring to determine if the road and traffic conditions are suitable and safe. This paper presents a node and link system to build a road monitoring system by considering the ODD(Operational Design Domain) characteristics. Considering scalability, the design is based on the existing ITS standard node-link system, and a method for expressing the monitoring target as a node and a link is presented. We further present a technique to classify and manage hazard risk into five levels, and a method to utilize node and link information when searching for and controlling the optimal route. Furthermore, we introduce an example of system implementation based on the proposed node and link system for Sejong City.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.6
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• pp.867-878
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• 2018

As the era of AVs (Automated Vehicles) comes to a close, many researches related to AVs have been conducted. Up until now, research on traffic flow impact of AVs has been the main topic, and research on traffic management for AVs is still in beginning stage. This study analyzed the effect of Dynamic Merge Control (DMC) in manual vehicle (MV) and AV environment at work zone. Dynamic Late Merge (DLM) and DLM with Dynamic Early Merge (DEM) are compared by simulation. Simulation results showed that DLM improves travel time and work zone throughput compared to no merge control case in both MV and AV environment. In the case of additional operation of DEM, the improvement effect was not observed in MV environment, but it was improved in AV environment. As a result, DMC operation in AV environment was as effective as the improvement in transition from MV to AV environment. Therefore congestion reduction at freeway work zone by DMC will be possible in future AV environment, and the improvement of DMC can be suggested.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.173-188
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• 2024

With the advancement of autonomous vehicle (AV) technology, autonomous driving on real roads has become feasible. However, there are challenges in achieving complete autonomy due to perceptual blind areas, which occur when the AV's sensory range or capabilities are limited or impaired by surrounding objects or environmental factors. This study aims to analyze AV accident patterns and safety issues of perceptual blind area that may occur in urban areas, with the goal of developing test scenarios for Level 4+ autonomous driving. It utilized AV accident data from the California Department of Motor Vehicles (DMV) to compare accident patterns and characteristics between AVs and conventional vehicles based on activation status of autonomous mode. It also categorized AV disengagement data to identify types and real-world cases of disengagements caused by perceptual blind areas. The analysis revealed that AVs exhibit different accident types due to their safe driving maneuvers, and three types of perceptual blind area scenarios were identified. The findings of this study serve as crucial foundational data for developing Level 4+ autonomous driving test scenarios, enabling the design of efficient strategies to mitigate perceptual blind areas in various scenarios. This, in turn, is expected to contribute to the effective evaluation and enhancement of AV driving safety on real roads.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.1
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• pp.63-73
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• 2024

Autonomous vehicles at Levels 3 to 5, currently under global research and development, seek to replace the driver's perception, judgment, and control processes with various sensors integrated into the vehicle. This integration enables artificial intelligence to autonomously perform the majority of driving tasks. However, autonomous vehicles currently obtain temporary driving permits, allowing them to operate on roads if they meet minimum criteria for autonomous judgment abilities set by individual countries. When autonomous vehicles become more widespread in the future, it is anticipated that buyers may not have high confidence in the ability of these vehicles to avoid hazardous situations due to the limitations of temporary driving permits. In this study, we propose a method for grading the judgment abilities of autonomous vehicles based on a driving simulator experiment comparing and evaluating drivers' abilities to avoid hazardous situations. The goal is to derive evaluation criteria that allow for grading based on specific scenarios and to propose a framework for grading autonomous vehicles. Thirty adults (25 males and 5 females) participated in the driving simulator experiment. The analysis of the experimental results involved K-means cluster analysis and independent sample t-tests, confirming the possibility of classifying the judgment abilities of autonomous vehicles and the statistical significance of such classifications. Enhancing confidence in the risk-avoidance capabilities of autonomous vehicles in future hazardous situations could be a significant contribution of this research.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1309-1312
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• 2015

본 논문에서는 현대 자율 주행 차량 경진대회에 적용되었던 LIDAR 기반의 다중 물체 추적 알고리즘을 소개한다. 물체 추적은 자율 주행 차량이 외부 환경을 인지하는데 중요한 역할을 한다. 본 논문의 물체 추적 알고리즘은 동시에 여러 개의 물체를 추적할 수 있도록 Multiple Data Association 방식을 사용하였고 순수하게 LIDAR만으로 동작하기 때문에 밤과 낮 모든 경우에 적용 가능하다. 알고리즘은 Clustering, Data Association, State Estimation, Data Arrangement 총 4단계로 이루어져 있으며 본 논문에서는 각 단계별로 알고리즘의 동작 방식을 소개한다. 실제 구현에는 Velodyne사의 HDL-32e이 사용되었고 실제 주행에서 교차로 내의 차량 추적 및 선행 차량의 동향을 추적하는데 적용되었다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.14-28
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• 2018

Thanks to technical improvement on the vehicle to vehicle communication and the intelligent transportation system, gradual introduction of the autonomous vehicles is expected soon in the market. The study analyzes the autonomous vehicles' impacts on the network efficiencies. In order to measure the network efficiencies, the study applies the sequential procedures that combines the microscopic and macroscopic simulations. The microscopic simulation attends to the capacity changes due to the autonomous vehicles' proportions on the roadway while the macroscopic simulation utilizes the simulation results in order to identify the network-wide improvement. As expected, the autonomous vehicles efficiently utilizes the existing capacity of the roadway than the human driving does. Particularly, the maximum capacity improvements are expected by the 190.5% on the expressway. The significant capacity change is observed when the autonomous vehicles' proportions are about 80% or more. These improvements are translated into the macroscopic model, which also yields overall network efficiency improvement by the autonomous vehicles' penetration. However, the study identifies that the market debut of the autonomous vehicles does not promise the free flow condition, which implies the possible needs of the system optimal routing scheme for the era of the autonomous vehicles.

• Journal of IKEEE
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• v.25 no.3
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• pp.565-570
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• 2021

This paper describes UL 4600, a new international safety standard to ensure safety of autonomous cars. Conventional vehicular safety standards such as ISO 26262 and ISO/PAS 21448 suffer from large limitations to be applied to autonomous cars, but UL 4600 exploits new approaches to be applied to autonomous cars. Conventional standards define various technological aspects to ensure safety and require manufacturers to certify these aspects. On the contrary, UL 4600 requires manufacturer to explain and prove why autonomous cars are safe. In UL 4600, (1) under specific environments where the system is designed to operate with, (2) claims should be defined to guarantee given safety, and (3) arguments should be suggested to satisfy given goals, and (3) evidences should be presented to prove given arguments. UL 4600 is technology-neutral since it does not require specific designs nor technologies. So UL 4600 only requires manufacturers to prove given safety goals regardless of methods and technologies. Also UL 4600 admits various cases of autonomous car field operations into the standard via feedback loop. So UL 4600 effectively maneuvers various dangers unknown at the time of standard establishment.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.312-313
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• 2007

본 논문은 자율주행로봇의 경로계획문제를 효율적으로 해결하기 위한 방법을 제안한다. 제안된 방법은 초기경로 생성과정과 초기경로를 개선하는 두 단계의 과정으로 구성된다. 효율적인 경로를 생성하기 위해서 최적화 문제를 해결하는 방법으로 잘 알려진 Dijkstra 알고리즘과 동적계획법(Dynamic Plogramming)을 적용한다. 그리고 제안된 방법의 효율성은 기존에 사용되는 경로계획 방법들과의 비교 시뮬레이션을 통해서 확인한다.