• Journal of the Korean Society of Systems Engineering
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• v.15 no.2
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• pp.1-8
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• 2019

Autonomous vehicles operated in the rough terrain environment must satisfy various technical requirements in order to improve the speed. Therefore, in order to design and implement a technical architecture that satisfies the requirements for speed improvement of autonomous vehicles, it is necessary to consider the overall technology of hardware and software to be mounted. In this study, the technical architecture of the autonomous vehicle operating in the rough terrain environment is presented. In order to realize high speed driving in pavement driving environment and other environment, it should be designed to improve the fast and accurate recognition performance and collect high quality database. and it should be determined the correct running speed from the running ability analysis and the frictional force estimation on the running road. We also improved synchronization performance by providing precise navigation information(time) to each hardware and software.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.346-350
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• 2023

Trajectory prediction is an essential element for driving autonomous vehicles, and various trajectory prediction models have emerged with the development of deep learning technology. Convolutional neural network (CNN) is the most commonly used neural network architecture for extracting the features of visual images, and the latest models exhibit high performances. This study was conducted to identify an efficient CNN backbone model among the components of deep learning models for trajectory prediction. We changed the existing CNN backbone network of multiple-trajectory prediction models used as feature extractors to various state-of-the-art CNN models. The experiment was conducted using nuScenes, which is a dataset used for the development of autonomous vehicles. The results of each model were compared using frequently used evaluation metrics for trajectory prediction. Analyzing the impact of the backbone can improve the performance of the trajectory prediction task. Investigating the influence of the backbone on multiple deep learning models can be a future challenge.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.6
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• pp.606-616
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• 2022

Semantic segmentation in autonomous driving for unstructured environments is challenging due to the presence of uneven terrains, unstructured class boundaries, irregular features and strong textures. Current off-road datasets exhibit difficulties like class imbalance and understanding of varying environmental topography. To overcome these issues, we propose a deep learning framework for semantic segmentation that involves a pooled class semantic segmentation with five classes. The evaluation of the framework is carried out on two off-road driving datasets, RUGD and TAS500. The results show that our proposed method achieves high accuracy and real-time performance.

• International journal of advanced smart convergence
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• v.10 no.4
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• pp.263-272
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• 2021

At a time when securing driving safety is the most important in the development and commercialization of autonomous vehicles, AI and big data-based algorithms are being studied to enhance and optimize the recognition and detection performance of various static and dynamic vehicles. However, there are many research cases to recognize it as the same vehicle by utilizing the unique advantages of radar and cameras, but they do not use deep learning image processing technology or detect only short distances as the same target due to radar performance problems. Radars can recognize vehicles without errors in situations such as night and fog, but it is not accurate even if the type of object is determined through RCS values, so accurate classification of the object through images such as cameras is required. Therefore, we propose a fusion-based vehicle recognition method that configures data sets that can be collected by radar device and camera device, calculates errors in the data sets, and recognizes them as the same target.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.17-27
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• 2012

This study provides development information on Omni-Wheel Drive Rider Robot, futuristic electric scooters, with autonomous driving systems that are used for people including the disabled and senior. Also, it is meaningful in suggesting alternatives to replace motorized wheelchairs or electric scooters for the future. Prior to development of Omni-Wheel Drive Rider Robot with autonomous driving systems, it surveyed 49 people, including 18 people who own electric scooters and 31 senior people who have not. The summary of the survey is as follows. First, inconveniences during riding and exiting and short mileage due and safety driving to problems of recharging batteries are the most urgent task. For these problems, the study shows that charging time of batteries, mileage, armrests, footrests, angle of a seat are the primary considerations. Second, drivers prefer joystick over steering wheels because of convenience in one-handed driving against dangers from footrest and carriageways sloping roads, paving blocks. One-handed driving can reduce driving fatigues with automatic stop systems. Moreover, the study suggests many design factors related to navigation systems, obstacle avoidance systems, omni-wheels, automatic cover-opening systems in rainy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.167-181
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• 2024

Utilization of a driving simulator in the development of autonomous driving technology allows us to perform various tests effectively in criticial environments, thereby reducing the development cost and efforts. However, there exists a serious drawback that the driving simulator has a big difference from the real environment, so a problem occurs when the autonomous driving algorithm developed using the driving simulator is applied directly to the real vehicle system. This is defined as so-called Sim2Real problem and can be classified into scenarios, sensor modeling, and vehicle dynamics. This Paper presensts on a method to solve the Sim2Real problem in autonomous driving simulator focusing on IMU sensor. In order to reduce the difference between emulated virtual IMU sensor real IMU sensor, IMU sensor emulation techniques through precision error modeling of IMU sensor are introduced. The error model of IMU sensors takes into account bias, scale factor, misalignmnet, and random walk by IMU sensor grades.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.51-56
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• 2020

This study examines the strategies employed by GM, who experienced bankruptcy in 2008. Specifically, we explore the autonomous driving-related technologies and execution, which GM began developing later than other car manufacturing companies. This study found that GM implemented aggressive M&A in search of vertical industrial integration for the development and production of autonomous vehicles. GM selected candidate firms to complement its technological gaps for the development and implementation of the autonomous vehicle. Secondly, GM achieved executive capacity by attempting to build a vertical integration in the wider scope of components, solution, service, and sales. Thirdly, the consistent governmental support and policies, such as the connected car project, M-City, and NCHRP Program expedited the development process. This study provides practical and policy implications for Korean companies and policymakers related to the automotive industry.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.1001-1014
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• 2018

Purpose: Autonomous driving cars, which are often represent the new convergence product, have been researched since the early years of 1900 but their safety assurance policies are yet to be implemented for real world practices. The primary purpose of this paper is to propose a modular concept based on which a safety assurance system can be designed and implemented for operating autonomous driving cars. Methods: We combine a set of key attributes of CE mark (European Assurance standard), E-Mark (Automobile safety assurance system), and A-SPICE (Automobile software assurance standard) into a modular approach. Results: Autonomous vehicles are emphasizing software safety, but there is no integrated safety certification standard for products and software. As such, there is complexity in the product and software safety certification process during the development phase. Using the concept of module, we were able to come up with an integrated safety certification system of product and software for practical uses in the future. Conclusion: Through the modular concept, both international and domestic standards policy stakeholders are expected to consider a new structure that can help the autonomous driving industries expedite their commercialization for the technology advanced market in the era of Industry 4.0.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.153-170
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• 2020

With the recent development of AI and ICT, autonomous vehicles are becoming a reality, and sales of the vehicles equipped with partial autonomous driving technology are also rapidly expanding. In this situation, technology research on autonomous vehicles has been actively conducted, but research on exploring the perception of autonomous vehicles from the user's perspective is relatively insufficient. Therefore, this study categorizes autonomous vehicle users into four types - , , , and . Then, it examines the characteristics of each type. For this purpose, we applied Q-methodology, a qualitative research method, to observe self-referent subjectivity of 32 P-samples using a Q-sample which consists of 34 statements. The results of our study have significance in that they provide domestic and global automakers with strategic directions for technological development and market expansion of autonomous vehicles, and academically provide hypotheses for subsequent quantitative research.

• Tunnel and Underground Space
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• v.32 no.3
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• pp.231-242
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• 2022

In this study, we developed a robot operating system (ROS)-based autonomous driving robot that estimates the robot's position in underground mines and drives and returns through multiple waypoints. Autonomous driving robots utilize SLAM (Simultaneous Localization And Mapping) technology to generate global maps of driving routes in advance. Thereafter, the shape of the wall measured through the LiDAR sensor and the global map are matched, and the data are fused through the AMCL (Adaptive Monte Carlo Localization) technique to correct the robot's position. In addition, it recognizes and avoids obstacles ahead through the LiDAR sensor. Using the developed autonomous driving robot, experiments were conducted on indoor experimental sites that simulated the underground mine site. As a result, it was confirmed that the autonomous driving robot sequentially drives through the multiple waypoints, avoids obstacles, and returns stably.