• Journal of the Ergonomics Society of Korea
• /
• v.31 no.5
• /
• pp.637-646
• /
• 2012

Objective: The aim of this study was to develop the adaptive device for severe physical disabilities using smart device in the driving simulator and its performance evaluation. Development of appropriate driving adaptive device for the people with serious physical limitation could contribute to maintain their community mobility. Background: There is lack of adaptive driving devices for the people with disabilities in Korea. However, if smart device systems like iPod and iPhone are used for driving a car, the people with serious physical limitations can improve their community mobility. Method: Both gyroscope and accelerometer from iPod were used to measure the tilted angle of the smart device for driving. Customized Labview program was also used to control three axis motors for steering wheel, accelerator and brake pedals. Thirteen subjects were involved in the experiment for performance evaluation of smart device in simulator. Five subjects had driver licenses. Another four subjects did not have driver licenses. Others were people with disabilities. Results: Average driving score of the normal group with driver license in the simulator increased 46.6% compared with the normal group without driver license and increased 30.4% compared with the disabled group(p<0.01). There was no significant difference in the average driving score between normal group without driver license and disabled group(p>0.05). Conclusion: The normal group with driver license showed significantly higher driving score than other groups. The normal group without driver license and disabled group could improve their driving skills with training in simulator. Application: If follow-up studies would be continued and applied in adapted vehicle for on road environment, many people with more severe disabilities could drive and improve the quality of life.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.11
• /
• pp.4268-4289
• /
• 2020

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

• Journal of Advanced Navigation Technology
• /
• v.16 no.3
• /
• pp.480-487
• /
• 2012

In Busan, the structural limitations of the road, is causing severe traffic congestion and low speed of the vehicle. So the existing traffic control system needs improvements to its structure. A study on Optimal Traffic Signal System and Improvement for User Oriented Public Transit Service are required. U-city is a city or region with ubiquitous information technology. All information systems are linked, and virtually everything is linked to an information technologies. U-Traffic goal is to maximize of traffic information services based on advanced information technology to integrate of transportation infrastructure. The objectives of this research are : a vehicle detection method through a variety of sensors, an algorithm of the traffic signal system, a design and implementation a simulator to compare between the fixed traffic signal and adaptive traffic signal system. This simulator will have allowed analysis techniques for the study of traffic control. Results of simulator test shows that traffic congestion can be some reduce.

• Journal of Auto-vehicle Safety Association
• /
• v.7 no.1
• /
• pp.33-39
• /
• 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 Korean Institute of Intelligent Systems
• /
• v.13 no.3
• /
• pp.300-309
• /
• 2003

The Japanese developed the first Car navigation system in 1981 with the advent of Honda, which was known as the car inertial navigation system. Now days, It is possible to search the shortest route to and from places and arrival time using the internet via cell phone to the driver based on GIS and GPS. However, even with a good navigation system, it losses the shortest route when there is an average speed of the vehicle being between S-15 kilometers. Therefore, in order to improve the vehicle waiting time and average vehicle speed, we are suggesting an optimal green time algorithm using fuzzy adaptive control, where there are different traffic intersection lengths, and lanes. In this paper, to be able to assist the driver and forecast the optimal traffic information with regards to the road conditions; dangerous roads, construction work and estimation of arrival time at their destination using internet.

• Journal of Broadcast Engineering
• /
• v.22 no.3
• /
• pp.282-294
• /
• 2017

In this paper, we propose a novel intelligent headlight control (IHC) system which is durable to various road lights and camera movement caused by vehicle driving. For detecting candidate light blobs, the region of interest (ROI) is decided as front ROI (FROI) and back ROI (BROI) by considering the camera geometry based on perspective range estimation model. Then, light blobs such as headlights, taillights of vehicles, reflection light as well as the surrounding road lighting are segmented using two different adaptive thresholding. From the number of segmented blobs, taillights are first detected using the redness checking and random forest classifier based on Haar-like feature. For the headlight and taillight classification, we use the random forest instead of popular support vector machine or convolutional neural networks for supporting fast learning and testing in real-life applications. Pairing is performed by using the predefined geometric rules, such as vertical coordinate similarity and association check between blobs. The proposed algorithm was successfully applied to various driving sequences in night-time, and the results show that the performance of the proposed algorithms is better than that of recent related works.

• The Journal of Korea Robotics Society
• /
• v.17 no.2
• /
• pp.198-208
• /
• 2022

In this paper, an autonomous driving system is implemented for the Segye AI Robot Race Competition that multiple vehicles drive simultaneously. By utilizing the ERP42-racing platform, RTK-GPS, and LiDAR sensors provided in the competition, we propose an autonomous driving system that can drive safely and quickly in a road environment with multiple vehicles. This system consists of a recognition, judgement, and control parts. In the recognition stage, vehicle localization and obstacle detection through waypoint-based LiDAR ROI were performed. In the judgement stage, target velocity setting and obstacle avoidance judgement are determined in consideration of the straight/curved section and the distance between the vehicle and the neighboring vehicle. In the control stage, adaptive cruise longitudinal velocity control based on safe distance and lateral velocity control based on pure-pursuit are performed. To overcome the limited experimental environment, simulation and partial actual experiments were conducted together to develop and verify the proposed algorithms. After that, we participated in the Segye AI Robot Race Competition and performed autonomous driving racing with verified algorithms.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.10 no.5
• /
• pp.325-333
• /
• 2015

Vision based night-time vehicle detection has been an emerging research field in various advanced driver assistance systems(ADAS) and automotive vehicle as well as automatic head-lamp control. In this paper, we propose night-time vehicle detection method based on multi-class support vector machine(SVM) that consists of thresholding, labeling, feature extraction, and multi-class SVM. Vehicle light candidate blobs are extracted by local mean based thresholding following by labeling process. Seven geometric and stochastic features are extracted from each candidate through the feature extraction step. Each candidate blob is classified into vehicle light or not by multi-class SVM. Four different multi-class SVM including one-against-all(OAA), one-against-one(OAO), top-down tree structured and bottom-up tree structured SVM classifiers are implemented and evaluated in terms of vehicle detection performances. Through the simulations tested on road video sequences, we prove that top-down tree structured and bottom-up tree structured SVM have relatively better performances than the others.

• Smart Media Journal
• /
• v.13 no.3
• /
• pp.18-26
• /
• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of Auto-vehicle Safety Association
• /
• v.10 no.2
• /
• pp.20-28
• /
• 2018

Currently, sharp increase of car is on the rise as a serious social problem due to loss of lives from car accident and environmental pollution. There is a study on ITS (Intelligent Transportation System) to seek coping measures. As for the commercialization of ITS, we aim for occupancy of world market through ASV (Advanced Safety Vehicle) related system development and international standardization. However, the domestic environment is very insufficient. Core factor technologies of ITS are Adaptive Cruise Control, Lane Keeping Assist System, Forward Collision Warning System, AEB (Autonomous Emergency Braking) system etc. These technologies are applied to cars to support driving of a driver. AEB system is stop the car automatically based on the result decided by the relative speed and distance with obstacle detected through sensor attached on car rather than depending on the driver. The purpose of AEB system is to measure the distance and speed of car and to prevent accident. Thus, AEB will be a system useful for prevention of accident by decreasing car accident along with the development of automobile technology. This study suggests a scenario to suggest a test evaluation method that accords with domestic environment and active response of international standard regarding the test evaluation method of AEB. Also, by setting the goal with function for distance, it suggests theoretic model according to the result. And the study aims to verify the theoretic evaluation standard per proposed scenario using car which is installed with AEB device through field car driving test on test road. It will be useful to utilize the suggested scenario and theoretical model when conducting AEB test evaluation.