https://scholar.dgist.ac.kr/handle/20.500.11750/15719 2026-08-03T12:13:09Z https://scholar.dgist.ac.kr/handle/20.500.11750/60561 Title: 자율주행 차량의 인공지능 기반 실시간 고장 진단 장치 및 방법 Author(s): 신관준; 김태연; 최경호; 문찬우; 손준성; 손승현 Abstract: 일 실시예에 따른 자율주행 차량의 인공지능 기반 실시간 고장 진단 장치는 자율주행 차량 내 네트워크를 통해 차량의 전반적인 동작 상태와 관련된 데이터를 수신하는 캔통신부 및 수신된 상기 데이터를 기초로 상기 자율주행 차량의 동작 상태의 이상 여부를 판단하는 고장 진단부를 포함하고 상기 고장 진단부는, 상기 데이터 중에서 상기 자율주행 차량의 자율주행 시스템과 관련된 자율주행 데이터를 추출하는 전처리부 및 상기 자율주행 데이터를 기초로 인공지능을 이용하여 상기 자율주행 시스템의 동작 상태의 이상 여부를 판단하는 고장 진단 모델을 포함한다. https://scholar.dgist.ac.kr/handle/20.500.11750/59051 Title: AVOIDANCE PATH GENERATION METHOD ON BASIS OF MULTI-SENSOR CONVERGENCE USING CONTROL INFRASTRUCTURE, AND CONTROL DEVICE Author(s): 김제석; 권순; 김예온; 최경호; 임용섭 Abstract: An avoidance path generation method on the basis of multi-sensor convergence using a control infrastructure comprises the steps in which: a control device receives first sensing data relating to a region of interest from sensors of the control infrastructure; the control device receives second sensing data relating to a peripheral area from a moving object which moves on a set path; the control device generates convergence data by converging the first sensing data and the second sensing data; the control device determines whether or not there is a risk factor in an area to which the moving object is to move, by means of the convergence data; and, if there is a risk factor, the control device determines an avoidance path enabling the moving object to avoid the risk factor. https://scholar.dgist.ac.kr/handle/20.500.11750/58135 Title: RainSD: Rain style diversification module for image synthesis enhancement using feature-level style distribution Author(s): Jeon, Hyeonjae; Seo, Junghyun; Kim, Taesoo; Son, Sungho; Lee, Jungki; Choi, Gyeungho; Lim, Yongseob Abstract: Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the spread of autonomous vehicles widely, it is important to address safety issues in this technology. Among various safety concerns, the sensor blockage problem by severe weather conditions can be one of the most frequent threats for multi-task learning-based perception algorithms during autonomous driving. To handle this problem, the importance of generating proper datasets is becoming more significant. In this paper, a synthetic road dataset with sensor blockage generated from real road dataset BDD100K is suggested in the format of BDD100K annotation. Rain streaks for each frame were made using an experimentally established equation and translated utilizing the image-to-image translation network based on style transfer. Using this dataset, the degradation of the diverse multitask networks for autonomous driving, such as lane detection, driving area segmentation, and traffic object detection, has been thoroughly evaluated and analyzed. The tendency of performance degradation of deep neural network-based perception systems for autonomous vehicles has been analyzed in depth. Finally, we discuss the limitation and future directions of deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation. © 2025 2025-03-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/57801 Title: CARLA 시뮬레이터 기반 합성 평가 데이터셋을 활용한 극한 폭우 상황에서의 심층 신경망을 이용한 차선 인식 성능 평가 Author(s): 전현재; 박성정; 손성호; 이정기; 안진웅; 최경호; 임용섭 Abstract: Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the autonomous vehicles to spread widely, it is important to properly deal with the safety issues on this technology. Among various safety concerns, the sensor blockage problem by severe weather conditions can be one of the most frequent threats for lane de-tection algorithms during autonomous driving. To handle this problem, the importance of the generation of proper datasets is becoming more significant. In this paper, a synthetic lane dataset with sensor blockage is suggested in the format of lane detection evaluation. Rain streaks for each frame were made by an experimentally established equation. Using this dataset, the degradation of the diverse lane detection methods has been verified. The trend of the per-formance degradation of deep neural network- based lane detection methods has been analyzed in depth. Finally, the limitation and the future directions of the network-based methods were presented. 2024-11-30T15:00:00Z