• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.84-90
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• 2022

With the popularization of autonomous driving technology, safety has emerged as a more important criterion. However, there are no assessment protocol or methods for AES (Autonomous Emergency Steering). So, this study proposes AES assessment protocol and scenario corresponding to collision avoidance Car-to-Car scenario of Euro NCAP in order to prepare for obstacles that appear after the emergency steering of LV (Leading Vehicle) avoiding obstacles in front of. Autoware-based autonomous driving stack is developed to test and simulate scenario in CARLA. Using developed stack, it is confirmed that obstacle avoidance is successfully performed in CARLA, and the AES performance of VUT (Vehicle Under Test) is evaluated by applying the proposed assessment protocol and scenario.

• Journal of IKEEE
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• v.23 no.1
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• pp.342-345
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• 2019

In autonomous car, significant progress has been achieved in technical aspects, but it is deadly slow to solve its various legal problems for commercialization of autonomous car. This paper surveys the road driving eligibility, a typical legal issue in autonomous car. Problems on current laws and regulations are analyzed, and some remedies are suggested. Technical development should be performed collaboratively with law and regulation revision, and understanding these legal issues would be very helpful to the engineers who develop autonomous cars.

• Transportation Technology and Policy
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• v.12 no.3
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• pp.7-13
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• 2015

• Journal of Auto-vehicle Safety Association
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• v.16 no.1
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• pp.55-61
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• 2024

The advancement of autonomous driving technology is expected to transform cars beyond mere transportation into multifunctional spaces for relaxation and entertainment. As autonomous driving technology becomes more sophisticated, with no need for direct driver control, the interior space of vehicles is anticipated to be utilized for various purposes. Consequently, the importance of car seats, the component most frequently interacted with by passengers during travel, is expected to significantly rise. However, existing car seats are designed according to a seated posture, necessitating verification for passenger safety and seat structure considerations in the context of autonomous driving, where comfortable postures may differ. For these reasons, it is anticipated that the seats of future autonomous vehicles will evolve with the incorporation of additional safety and convenience features. In this study, a three-axis car simulator was employed to investigate seat angles for comfortable postures of passengers in autonomous driving scenarios. Representative postures were identified to enhance passenger convenience. Furthermore, functional design factors contributing to passenger comfort were applied to conduct seat design, seat structure, and collision analysis, with an analysis of the interrelationships among design factors.

• The Journal of the Convergence on Culture Technology
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• v.3 no.2
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• pp.49-54
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• 2017

In Korea, partial self-driving feature is added on Genesis G80, Tivoli 2017, and others, and full implementation is under evaluation. Tesla already completed test for full self-driving car, Tesla Model 'X'. Further adoption of self-driving car in market will bring benefits to the elderly and disabled, meanwhile traffic accident will be decreased. However, related regulations for traffic accident with autonomous car including ethical responsibility is not fully established yet. In addition, security and privacy issue of self-driving cars should be improved as well. In this paper, domestic researches and analysis status on autonomous car will be summarized, and proper activation model will be proposed for the previously described issues.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.30-36
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• 2019

This paper suggests a car-following algorithm for urban environment, with multiple target candidates. Until now, advanced driver assistant systems (ADASs) and self-driving technologies have been researched to cope with diverse possible scenarios. Among them, car-following driving has been formed the groundwork of autonomous vehicle for its integrity and flexibility to other modes such as smart cruise system (SCC) and platooning. Although the field has a rich history, most researches has been focused on the shape of target trajectory, such as the order of interpolated polynomial, in simple single-lane situation. However, to introduce the car-following mode in urban environment, realistic situation should be reflected: multi-lane road, target's unstable driving tendency, obstacles. Therefore, the suggested car-following system includes both in-lane preceding vehicle and other factors such as side-lane targets. The algorithm is comprised of three parts: path candidate generation and optimal trajectory selection. In the first part, initial guesses of desired paths are calculated as polynomial function connecting host vehicle's state and vicinal vehicle's predicted future states. In the second part, final target trajectory is selected using quadratic cost function reflecting safeness, control input efficiency, and initial objective such as velocity. Finally, adjusted path and control input are calculated using model predictive control (MPC). The suggested algorithm's performance is verified using off-line simulation using Matlab; the results shows reasonable car-following motion planning.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.49-58
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• 2022

With the recent start of the 4th Industrial Revolution, markets related to autonomous driving are rapidly developing. In order to understand the rapidly developed technology trend of autonomous driving technology, we would like to investigate the characteristics and differences of level 0 to level 5 of autonomous driving. The overall configuration, recognition technology, and auxiliary technologies of autonomous vehicles are analyzed, and through this, the structure and algorithm of autonomous driving technology are identified. In addition, by manufacturing a simulated autonomous RC car using an ultrasonic sensor and a camera, the necessity of recognition technology and auxiliary technology is identified.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.210-218
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• 2021

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.105-112
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• 2018

Along with the recent technological development, autonomous vehicles are being commercialized. but The accident of autonomous driving car is becoming an issue, and safety problem of autonomous driving car is becoming a hot topic. Also There are currently no specific guidelines for clear laws and security ethics. These guidelines require a lot of information and experience. This study establishes basic guidelines based on cases of accidents from past to present. This study suggests security considerations through case study of security ethics in autonomous car accident.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.172-185
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• 2021

As the era of autonomous driving approaches, it is expected to have a significant impact on our lives. When autonomous driving cars emerge, it is necessary to develop an index that can evaluate autonomous driving cars as it enhance the productive value of the car by reducing the burden on the driver. This study analyzed how the autonomous driving era affects commuting time and commuting time satisfaction among office goers using a car in Gyeonggi-do. First, a nonlinear relationship (V) was derived for the commuting time and commuting time satisfaction. Here, the factors affecting commuting time satisfaction were analyzed through a binomial logistic model, centered on the sample belonging to the nonlinear section (70 minutes or more for commuting time), which is likely to be affected by the autonomous driving era. The analysis results show that the variables affected by the autonomous driving era were health, sleeping hours, working hours, and leisure time. Since the emergence of autonomous driving cars is highly likely to improve the influencing variables, long-distance commuters are likely to feel higher commuting time satisfaction.