• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.711-720
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• 2012

In this paper, we introduce the implementation of driver fatigue monitering system and its result. Input video device is selected commercially available web-cam camera. Haar transform is used to face detection and adopted illumination normalization is used for arbitrary illumination conditions. Facial image through illumination normalization is extracted using Haar face features easily. Eye candidate area through illumination normalization can be reduced by anthropometric measurement and eye detection is performed by PCA and Circle Mask mixture model. This methods achieve robust eye detection on arbitrary illumination changing conditions. Drowsiness state is determined by the level on illumination normalize eye images by a simple calculation. Our system alarms and operates seatbelt on vibration through controller area network(CAN) when the driver's doze level is detected. Our algorithm is implemented with low computation complexity and high recognition rate. We achieve 97% of correct detection rate through in-car environment experiments.

• Journal of IKEEE
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• v.23 no.3
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• pp.844-851
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• 2019

As wearable technology is becoming more common and a part of our lives, there have been many efforts to offer various smart services with wearable devices, such as motion recognition, safety of driving, and so on. In this paper, we present a method that exploits the 9-axis inertial sensors embedded in a smartwatch to identify whether the user is a vehicle driver or not and to estimate the steering wheel turning direction in the vehicle. The system consists of three components: (i) position recognition, (ii) driver recognition, and (iii) steering-wheel turning detection components. We have developed a prototype system for detecting user's motion with Arduino boards and IMU sensors. Our experiments show high accuracy in recognizing the driver and in estimating the wheel rotation angle. The average experimental error was $11.77^{\circ}$ which is small enough to perceiver the turning direction of steering-wheel.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.103-120
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• 2020

Using driving simulation, this study investigated the effects of driver's trust in the adaptive cruise control (ACC) system and road density on driver's workload and situation awareness. The drivers were allocated into one of four experimental conditions manipulated by ACC system trust level (trust-increased vs. trust-decreased) and road congestion (high vs. low). The workload and situational awareness of the participants were measured as dependent variables. The results showed followings. First, trust-decreased group for the ACC system had significantly lower trust scores for the system in all of the measurement items, including reducing the driving load and securing safe driving due to the use of this system, than the trust-increased group. Second, the trust-decreased group showed a slower reaction time in the secondary tasks and higher subjective workload than trust-increased group. Third, in contrast, the situational awareness for the driving situation was significantly higher in the trust-decreased group than trust-increased group. The results of this study showed that the driver's trust in the ACC system can affect the various information processing performed while driving. Also, these results suggest that trust in the user's system should be considered as an important variable in the design of an automated driving assistance system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.3
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• pp.112-123
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• 2012

Different from general roads, intersections are the points where roads having different geometric structure and traffic operation system are met, and thereby they have complicated road structure and environmental factors. Various changes in driving patterns such as collision between vehicles approaching from roads adjacent to intersections, sudden stop of vehicles upon stop sign, quick start upon green lights kept increasing traffic accidents. It is known that traffic accidents are mainly derived from human factors. This study, in order to find out factors affecting drivers' behaviors within intersections, measured physiological responses such as brain wave, sight, driving speed, and so on by using state-of-the-art measuring device. As to concentration brain wave at individual intersections, it was found out that brain wave of testes was higher at main Arterial and accident-prone intersections compared with that of subsidiary Arterial. In addition, it was detected that drivers' visual activity was widely distributed at accident-prone intersections, meaning that it enhanced cautious driving from nearby vehicles. As to major factors causing drivers' workloads, factors from nearby vehicles such as deceleration, acceleration, lane change of nearby vehicles appeared as direct factors causing drivers' workloads, clarifying that these factors were closely related to causes of traffic accidents at intersections. Results of this study are expected to be used as basic data for evaluation of safety at intersections in consideration of physiological response of drivers.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.1
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• pp.31-40
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• 2012

This paper proposes a visual landmark, and presents a parking assistance system using the landmarks. The visual landmark is a feature corresponding to the parking slots, it must be selected considering the parking lot's environment. The parking lot has simple repetitive pattern environment without noticeable features. The previous landmarks are not proper to the parking lot's environment. We propose the visual landmark for this environment. We estimate the vehicle's location using the proposed landmarks, and expect the vehicle's trajectory according to the vehicle's state. The system's inputs are images from the camera fixed to the vehicle. The presented system estimates the vehicle's location using the input images, and assists a driver through displaying the expected vehicle's trajectory from the steering angle. The experimental results showed the proposed landmark's performance and the parking assistance system's performance.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.1-9
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• 2006

Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.544-551
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• 2015

In this paper, the design and implementation results of the optical flow estimator (OFE) for moving object detection (MOD) in advanced driver assistance system (ADAS). In the proposed design, Brox's algorithm with global optimization is considered, which shows the high performance in the vehicle environment. In addition, Cholesky factorization is applied to solve Euler-Lagrange equation in Brox's algorithm. Also, shift register bank is incorporated to reduce memory access rate. The proposed optical flow estimator was designed with Verilog-HDL, and FPGA board was used for the real-time verification. Implementation results show that the proposed optical flow estimator includes the logic slices of 40.4K, 155 DSP48s, and block memory of 11,290Kbits.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2532-2537
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• 2012

In recent, the researches on driver assistance systems have been actively performed with development of vehicle industry. AVM(Around View Monitoring) Systems, a part of these systems, have been researching and developing. Existing AVM systems have been developed in the forms of embedded systems or a SoC (System on Chip) to provide view around vehicle in real time. However, if Car PC is equipped with in vehicle, AVM can be developed using only software without additional cost. In this study, we implemented AVM system which provides location information by adding the informations such as latitude, longitude and speed to functions of "Car PC" based AVM system. If storing function is added to the AVM system implemented in this study which provides location information, the system with storing function can be used as AVM black box system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.157-163
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• 2020

Recently, fine dust has been known to cause cardiovascular diseases here, raising interest in ways to reduce emissions by efficiently using fuel from cars that cause air pollution. Accordingly, a driving assistance system was developed to save fuel by eco-driving and improve the driver's bad driving habits. The system was developed using raspberry pi, arduino and Android. Using RPM, speed, fuel injection information obtained from OBD-II, and gyro-sensor values, Fuel-Cut is induced to create an optimal inertial driving environment. It also provides various information system such as weather, driving environment, and preventing drowsy driving through GUI and voice recognition functions. It is possible to check driving records and vehicle fault information using Android application and has low overhead for message transmission using MQTT protocol optimized for IoT environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.563-570
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• 2024

This paper proposes a method to recognize and track drivable lane areas to assist the driver. The main topic is designing a deep-based network that predicts drivable road areas using computer vision and deep learning technology based on images acquired in real time through a camera installed in the center of the windshield inside the vehicle. This study aims to develop a new model trained with data directly obtained from cameras using the YOLO algorithm. It is expected to play a role in assisting the driver's driving by visualizing the exact location of the vehicle on the actual road consistent with the actual image and displaying and tracking the drivable lane area. As a result of the experiment, it was possible to track the drivable road area in most cases, but in bad weather such as heavy rain at night, there were cases where lanes were not accurately recognized, so improvement in model performance is needed to solve this problem.