• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.9 s.351
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• pp.49-59
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• 2006

DAS(Driver Assistance Systems) support the driver's decision making to increase safety and comfort by issuing the naming signals or even exert the active control in case of dangerous conditions. Most previous research and products intend to offer only a single warning service like the lane departure warning, collision warning, lane change assistance, etc. Although these functions elevate the driving safety and convenience to a certain degree, New type of DAS will be developed to integrate all the important functions with an efficient HMI (Human-Machine Interface) framework for various driving conditions. We propose an all-around sensing based on the integrated DAS that can also remove the blind spots using 2 cameras and 8 sonars, recognize the driving environment by lane and vehicle detection, construct a novel birds-eye HMI for easy comprehension. it can give proper warning in case of imminent danger.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.174-192
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• 2021

The LIDAR sensor, which provides higher cognitive performance than cameras and radar, is difficult to apply to ADAS or autonomous driving because of its high price. On the other hand, as the price is decreasing rapidly, expectations are rising to improve existing autonomous driving functions by taking advantage of the LIDAR sensor. In level 3 autonomous vehicles, when a dangerous situation in the cognitive module occurs due to a sensor defect or sensor limit, the driver must take control of the vehicle for manual driving. If the driver does not respond to the request, the system must automatically kick in and implement a minimum risk maneuver to maintain the risk within a tolerable level. In this study, based on this background, a LIDAR-based LKS MRM algorithm was developed for the case when the normal operation of LKS was not possible due to troubles in the cognitive system. From point cloud data collected by LIDAR, the algorithm generates the trajectory of the vehicle in front through object clustering and converts it to the target waypoints of its own. Hence, if the camera-based LKS is not operating normally, LIDAR-based path tracking control is performed as MRM. The HAZOP method was used to identify the risk sources in the LKS cognitive systems. B, and based on this, test scenarios were derived and used in the validation process by simulation. The simulation results indicated that the LIDAR-based LKS MRM algorithm of this study prevents lane departure in dangerous situations caused by various problems or difficulties in the LKS cognitive systems and could prevent possible traffic accidents.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.113-120
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• 2014

In this paper, we examine Asymmetric Multi-Processing Environment to provide LDW service. Asymmetric Multi-Processing Environment consists of high-speed MCU to support rapid image processing and low-speed MCU for controlling with other ECU at the control domain. Also we designed rapid image process application and LDW application Software Component(SW-C) according to the development process rule of AUTOSAR. To communicate between two MCUs, timer based polling based IPC was designed. Also to communicate with other ECUs(Electronic Control Units), we designed CAN messages to provide alarm information and receiving CAN message to catch the Turn signal. We confirm the possibility of the various ADAS development using an Asymmetric Multi-Processing Environment and AUTOSAR platform. We also expect providing ISO 26262 functional safety.

• Journal of Korean Society of Transportation
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• v.34 no.6
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• pp.499-509
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• 2016

The pedestrian traffic flow has more complicated microscopic features than vehicular traffic flow. Without any designated lanes or any guidance, pedestrians naturally move and change their routes in two dimensional domain with ease. Thus the assessment of pedestrian comfort level should be considering the microscopic features of pedestrian flow. This study is aimed at developing pedestrian comfort criteria based upon pedestrian flow simulation model. This study suggests three criteria to determine pedestrian comfort level; the deviation of route, the acceleration of walk, and the number of collision. Each criterion, which can address the unique walking patterns of pedestrian flow, is represented as each different function with respect to traffic flow rate. The criteria can be the additional indicators to determine the level of service of pedestrian flow together with traffic flow rate and walking speed.

• International Journal of Highway Engineering
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• v.12 no.1
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• pp.39-46
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• 2010

Delineator is a useful device to support driver's safer maneuver. Effective placement and operation of the delineator would lead to prevent traffic accidents on the roads. This study evaluates the effectiveness of parameters associated with delineator placement and operation, which include spacing, height and size, from the point of user's satisfaction. Also, this study devises a methodology for determining such parameters using binary logistic regression technique. The proposed model is capable of producing probabilistic measure of user's satisfaction according to the various parameters. The outcome of this study would be useful fundamentals for more effective placement and operation of delineators.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.235-248
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• 2013

In this study, the effects of driver's neuroticism on situation awareness(SA), eye movement(EM), and driving performance were examined. In Experiment 1, in which static driving scenes were presented, it was found that the drivers of higher neuroticism showed shorter percentage of dwell time and less percentage of fixation counts for the peripheral area than those of lower neuroticism, although SA scores yielded no difference between the two neuroticism groups. In Experiment 2, differences in the drivers' driving performances as well as SA and EM as a function of neuroticism were compared using driving simulation. The results of Experiment 2 showed the followings. First, contrasting to Experiment 1, the high neuroticism group showed lower SA score than low neuroticism group. Second, the pattern of EM was similar to those of Experiment 1. Third, high neuroticism group deviated more from center of road, and drove slower than low neuroticism group.

• Journal of Advanced Navigation Technology
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• v.18 no.1
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• pp.14-21
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• 2014

In this paper, we devise a new ontology-based context-aware system to recognize the smart car information, in which driver's intent is utilized by information of car, driver, environment as well as driving state, driver state. So proposed system can handle dynamically risk changes by adding real-time situational awareness information. We utilize the camera image recognition technology for context-aware intelligent vehicle driving information, and implement information acquisition scheme OBD-II protocol to acquire vehicle's information. Experiments confirm that the proposed advanced driver safety assist system outperforms the conventional system, which only utilizes the information of vehicle, driver, and environmental information, to support the service of a high-speed driving, lane-departure service and emergency braking situation awareness.

• Journal of Auto-vehicle Safety Association
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• v.7 no.1
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• pp.33-39
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• 2015

This study examined the drivers' acceptance level of various active safety features with Korean drivers on Seoul urban and city roads. The test vehicle, 2013 Cadillac ATS, was equipped with FCA(Forward Collision Alert), LDW(Lane Departure Warning), SBZA(Side Blind Zone Alert), FRPA(Front/Rear Park Assist), RCTA(Rear Cross Traffic Alert), ACC(Adaptive Cruise Control), and AEB(Autonomous Emergency Braking). Participants had chances to run the tests on those systems in the parking lot accompanied by the 106km long stretch of predetermined route including local road and interurban highway in Seoul and Gyeonggi-do under normal traffic flowing environment. After the test, participants completed a series of questionnaires about the features they experienced. The results revealed that RCTA and SBZA systems received more favourable ratings compared to the other features in avoiding crashes. The respondents preferred sound alerts to haptic ones even though haptic warning methods were better in providing directional information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.235-244
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• 2016

Automotive symbols are more widely needed for new, convenient driving devices in automobiles. Good automotive symbols should be detectable, identifiable at first glance, easily learned, recognizable, and produce quick responses after practice. In this paper, a methodology for developing and evaluating automotive symbols is suggested. It includes multiple tests, such as comprehension, perceptual quality, appropriateness, and integrated evaluation. 28 symbols were tested and evaluated by the suggested methodology for convenient driving systems, such as a lane departure warning system (LDWS), cruise control (CCS), and a collision warning system (CWS). Most of the KS R ISO 2575 symbols had higher scores of comprehension, perceptual quality, and appropriateness, but the sunroof and camera symbols had lower scores. Standard symbols with several new functions should be developed. This methodology could be useful for developing and evaluating automotive symbols.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.65-70
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• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.