• Journal of Korean Society of Transportation
• /
• v.20 no.4
• /
• pp.19-26
• /
• 2002

The purpose of this paper is to study the fundamental characteristics of bicycle flows. Several experiments were conducted to obtain the characteristics of bicycle flows, speed variation along a curve radius, bicycle driver's travelling territory and saturation flow rate at signalized intersection. Bicycle facilities are categorized into uninterrupted and interrupted, the capacity of uninterrupted is approximately 5000bic/h, and that of the interrupted (at signalized intersection) is approximately 3000bic/h, when a curve radius is over 20m, bicycle speed is not increasing. Bicycle driver's travelling territory is used to occupancy area, it is the same concept as pedestrian's. Bicycle occupancy area is to be divided into circulation zone, comfort zone. and collision zone. Circulation zone is over 2.21$\times$4.1m and collision zone is less than 0.96$\times$2.47m. Comfort zone is defined as intermediate state between two zones.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.4
• /
• pp.304-310
• /
• 2017

In this paper, we propose the architecture of an IPM based lane detector for autonomous vehicles to detect and control the driving route along the curved lane. In the IPM image, we divide the area into two fields, Far/Near Field, and the lane candidate region is detected using the Hough transform to perform the matching for the curved lane. In autonomous vehicles, various algorithms must be embedded in the system. To reduce the system resources, we proposed a method to minimize the number of memory accesses to the image and various parameters on the external memory. The proposed circuit has 96% lane recognition rate and occupies 16% LUT, 5.9% FF and 29% BRAM in Xilinx XC7Z020. It processes Full-HD image at a rate of 42 fps at a 100 MHz operating clock.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.11a
• /
• pp.486-487
• /
• 2012

본 논문에서는 차량 내에 설치된 카메라를 이용하여 전방차량을 인식하는 FCW(Forward Collision Warning)시스템에서 주행 중인 전방 차량을 추적하는 알고리즘을 제안한다. 전방 차량의 후보 영역을 검출하기 위해 Haar-Adaboost를 이용하였으며 검색된 차량 후보 영역 내의 에지 정보를 이용하여 차량 후보 영역을 필터링하였다. 필터링된 차량 영역은 영역기반과 Kalman 예측치를 이용하여 차량을 추적하는 방법으로 차량 검색기가 차량 영역을 검색하지 못하는 경우 Kalman 예측치를 통해 차량 후보 영역을 예측하고 예측된 차량 영역을 검증함으로써 효율적인 전방 차량 인식이 가능하였다. 본 제안 방법을 실험한 결과 이전 프레임에서 추적되던 차량 후보 영역이 현재 프레임에서 Haar-Adaboost가 차량 후보 영역을 검색하지 못하는 경우에 영역기반과 Kalman 예측치를 통하여 현재 프레임에서 전방차량을 연속적으로 추적하는 것을 확인하였다. 본 제안 방법은 영상을 이용한 FCW 시스템에 사용될 수 있을것으로 사료된다.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.2
• /
• pp.214-219
• /
• 2007

In this paper, a new magnetic position sensing mettled for autonomous vehicle and robot guidance is presented. In autonomous vehicle and robot control, position sensing is an important task for the identification of their locations, such as the current position within a trajectory. The magnet based autonomous vehicle and robot was identified position via magnetic materials. In the magnetic sensing system, the Earth field is one of the largest disturbance. To removal of the Earth field, this paper proposes 1-dimensional magnetic field sensors array and develops precise petition sensing system using linear operating region of the magnetic field sensor. This proposal is verified a feasible magnetic position sensing system for autonomous vehicle and robot guidance by the experimental results.

• Journal of the KSME
• /
• v.32 no.7
• /
• pp.613-619
• /
• 1992

짧은 시간의 주행 실험과 랜덤 응답 해석을 통한 차체의 피로수명을 해석하는 방법을 제시하 였다. 주파수 영역에서 다축 응력을 폰 미제스 응력으로 변환시키고 응력 진폭의 확률 분포를 와이블 분포를 고려하여 처리함으로써 더욱 신뢰도가 큰 해석이 되도록 하였다. 여기에 제시된 해석 과정은 일반적으로 적용 가능한 것이므로, 양산 전 단계의 차체 피로 설계에 좋은 참고가 될 것이다.

• Journal of Broadcast Engineering
• /
• v.28 no.2
• /
• pp.230-237
• /
• 2023

Self-driving is a key technology of the fourth industry and can be applied to various places such as cars, drones, cars, and robots. Among them, localiztion is one of the key technologies for implementing autonomous driving as a technology that identifies the location of objects or users using GPS, sensors, and maps. Locilization can be made using GPS or LIDAR, but it is very expensive and heavy equipment must be mounted, and precise location estimation is difficult for places with radio interference such as underground or tunnels. In this paper, to compensate for this, we proposes an image retrieval using attention module and image segmentation maps using color images acquired with low-cost vision cameras as an input.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.21 no.5
• /
• pp.197-217
• /
• 2022

As automated vehicle(AV) accidents continue to occur, the importance of safety verification to ensure the safety and reliability of automated driving system(ADS) is being emphasized. In order to encure safety and reliability, it is necessary to define an operational design domain(ODD) of the ADS and verify the safety of the ADS while evaluating its ability to respond in situations outside of the ODD. To this, international associations such as SAE, BSI, NHTSA, ISO, etc. stipulate ODD standards. However, in Korea, there is no standard for the ODD, so automated vehicles's ODD expression method and safety verification and evaluation are not properly conducted. Therefore, this study analyzed overseas ODD standards and selected suitable ODD for safety verification and evaluation, and presented evaluation elements for ADS safety verification and evaluation. In particular, evaluation elements were selected by analyzing the evaluation environment of the automated driving experimental city (K-City) that supports the development of ADS technology.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2016.06a
• /
• pp.194-197
• /
• 2016

자동차 산업이 발전하면서 안정적인 주행과 운전자의 편의성을 위한 지능형운전자보조시스템인 ADAS (Advanced Driver Assistance System)가 이슈가 되고 있다. 차선인식의 결과에 따라 차선이탈 경고시스템의 성능이 달라지기 때문에 차선인식은 ADAS에서 매우 중요한 핵심적인 기술이라 할 수 있다. 이에 본 논문에서는 그림자 영역과 같이 밝기의 분포가 균일하지 않는 환경에서 강인하게 동작하는 차선인식 알고리즘을 제안하였다, 지역적인 밝기 특징을 고려하여 차선에 해당하는 특징점을 추출하며, 추출된 특징점 가운데 이상치(outlier)를 제거하기 위해 RANSAC (RANdom SAmple Consensus) 알고리즘을 이용하여 차선을 검출한다. 또한 RANSAC 알고리즘에서 신뢰도가 높은 차선이 검출되면 그 주위에 특징점을 추출하기 위한 관심영역을 설정함으로써 안정적인 차선 검출이 가능하도록 하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.6
• /
• pp.313-330
• /
• 2021

In an autonomous driving system equipped with a camera and lidar, depth completion techniques enable dense depth estimation. In particular, using self-supervised learning it is possible to train the depth completion network even without ground truth. In actual autonomous driving, such depth completion should have very short latency as it is the input of other algorithms. So, rather than complicate the network structure to increase the accuracy like previous studies, this paper focuses on network latency. We design a U-Net type network with RegNet encoders optimized for GPU computation. Instead, this paper presents several techniques that can increase accuracy during the process of self-supervised learning. The proposed techniques increase the robustness to unreliable lidar inputs. Also, they improve the depth quality for edge and sky regions based on the semantic information extracted in advance. Our experiments confirm that our model is very lightweight (2.42 ms at 1280x480) but resistant to noise and has qualities close to the latest studies.

• Journal of Platform Technology
• /
• v.11 no.3
• /
• pp.24-31
• /
• 2023

Recently, interest in UAM (Urban Air Mobility, UAM), which can take off and land vertically in the operation of urban air transportation systems, has been increasing. Therefore, various start-up companies are developing related technologies as eco-friendly future transportation with advanced technology. However, studies on ways to increase safety in the operation of UAM are still insignificant. In particular, efforts are more urgent to improve the safety of risks generated in the process of attempting to land in the city center by UAM equipped with autonomous driving. Accordingly, this study proposes a plan to safely land by avoiding dangerous region that interfere when autonomous UAM attempts to land in the city center. To this end, first, the latitude and longitude coordinate values of dangerous objects observed by the sense of the UAM are calculated. Based on this, we proposed to convert the coordinates of the distorted planar image from the 3D image to latitude and longitude and then use the calculated latitude and longitude to compare the pre-learned feature descriptor with the HOG (Histogram of Oriented Gradients, HOG) feature descriptor to extract the dangerous Region. Although the dangerous region could not be completely extracted, generally satisfactory results were obtained. Accordingly, the proposed research method reduces the enormous cost of selecting a take-off and landing site for UAM equipped with autonomous driving technology and contribute to basic measures to reduce risk increase safety when attempting to land in complex environments such as urban areas.