• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.1
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• pp.18-23
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• 2011

We have investigated the Intelligent Shift-map Module(ISM) to improve some problems in the conventional Automatic Transmission(AT) for automobiles. The typical AT lacks flexibility regarding the shift point because it does not consider the driver's habits and inclinations. Also it often is occurred phenomenon like kick-down. Therefore, we designed a decision module which considers the driving style of the individual driver. The driving style was determined by the inclination of the driver and the driving technique using actual automobile data. The Hierarchical Neural Network(HNN) was applied in generating an intelligent shift map with Multilayer Neural Network(MNN). It was found that the proposed ISM provided a suitable shift point and time because the necessary toque and velocity of the automobile was considered along with the driving style of each driver when designing the ISM.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.251-259
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• 2023

In the path traveling of differential-drive robots, the steering controller plays an important role in determining the path-following performance. When a robot with a pure-pursuit algorithm is used to continuously drive a right-angled driving path in an unstructured environment without turning in place, the robot cannot accurately follow the right-angled path and stops driving due to the ground and motor load caused by turning. In the case of pure-pursuit, only the current robot position and the steering angle to the current target path point are generated, and the steering component does not reflect the speed plan, which requires improvement for precise path following. In this study, we propose a driving algorithm for differentially driven robots that enables precise path following by planning the driving speed using the radius of curvature and fusing the planned speed with the steering angle of the existing pure-pursuit controller, similar to the Model Predict Control control that reflects speed planning. When speed planning is applied, the robot slows down before entering a right-angle path and returns to the input speed when leaving the right-angle path. The pure-pursuit controller then fuses the steering angle calculated at each path point with the accelerated and decelerated velocity to achieve more precise following of the orthogonal path.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.3
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• pp.80-96
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• 2024

This study conducted over two rounds Delphi survey targeting experts in the fields of roads, transportation, automobiles, intelligent transport systems(ITS), and communications to analyze the direction of change in road design, operation, and systems in the Lv.4/4+ autonomous driving era. To construct the Delphi survey items, a basic survey was conducted. The first and second Delphi surveys targeted experts in fields closely related to autonomous driving. The results of the survey revealed an expectation that in the future, transportation operations would change focus to shared autonomous vehicle(AV) and public transportation transfers, and there would be corresponding changes in the cross-sectional configuration. Exclusive lanes for AV are expected to be introduced commencing from highways and general national roads, and there was a consensus that they should be installed in the center of the road rather than on the sides and would use the same lane width as that of the existing road. In terms of facilities for AVs, it was found that it would be necessary to expand pedestrian-vehicle separation facilities, insert communication chips into facilities for location recognition, advance precision maps, and expand roadside communication facilities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.147-161
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• 2021

The purpose of this study was to investigate the psychological state and driving safety of drivers driving around the truck platoon driving. Using the driving simulator, the experimental environment was constructed with the situation of changing lanes to the platoon and driving within the platoon. We tried to qualitatively and quantitatively analyze the driver's psychological state and driving safety through simulation driving experiments. As a result, in the case of the older driver group, there were many cases where they judged themselves to be driving safely, even though they were driving dangerously in the actual lane change to the platoon or driving within the platoon. In particular, this group showed that the narrower the distance between vehicles, the greater the misrecognition. The results of this study are expected to be useful in deriving the optimum interval when the interval between platooning of trucks needs to be temporarily extended.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1562-1582
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• 2024

The prediction of pedestrian trajectory is conducive to reducing traffic accidents and protecting pedestrian safety, which is crucial to the task of intelligent driving. The existing methods mainly use the past pedestrian trajectory to predict the future deterministic pedestrian trajectory, ignoring pedestrian intention and trajectory diversity. This paper proposes a multi-modal trajectory prediction model that introduces pedestrian intention. Unlike previous work, our model makes multi-modal goal-conditioned trajectory pedestrian prediction based on the past pedestrian trajectory and pedestrian intention. At the same time, we propose a novel Gate Recurrent Unit (GRU) to process intention information dynamically. Compared with traditional GRU, our GRU adds an intention unit and an intention gate, in which the intention unit is used to dynamically process pedestrian intention, and the intention gate is used to control the intensity of intention information. The experimental results on two first-person traffic datasets (JAAD and PIE) show that our model is superior to the most advanced methods (Improved by 30.4% on MSE_0.5s and 9.8% on MSE_1.5s for the PIE dataset; Improved by 15.8% on MSE_0.5s and 13.5% on MSE_1.5s for the JAAD dataset). Our multi-modal trajectory prediction model combines pedestrian intention that varies at each prediction time step and can more comprehensively consider the diversity of pedestrian trajectories. Our method, validated through experiments, proves to be highly effective in pedestrian trajectory prediction tasks, contributing to improving traffic safety and the reliability of intelligent driving systems.

• The Magazine of the IEIE
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• v.28 no.5
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• pp.55-62
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• 2001

In this paper, we introduc current research and development trend of intelligent vehicls and its application to driving suport system. We introduce some core technology required for developing thee systems and explain how it works. Moreover, some items related with ehicle information system and the intelligent vehicles will be introduced. Since the vehicle is very complex system, there are many problems in developing these systems, especially in developing intelligent vehicle. The main purpose of this is to dscuss these problems and find good solution.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.149-161
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• 2009

Electronic control systems of vehicle are rapidly developed to keep balance of a driver`s safety and the legal, social needs. The driver assistance systems are putted into practical use according to the cost drop in hardware and highly efficient sensor, etc. This study has developed a lane and vehicle detection program using CCD camera. The Risky Driving Analysis Program based on vision systems is developed by combining a risky driving detection algorithm formed in previous study with lane and vehicle detection program suggested in this study. Risky driving detection programs developed in this study with information coming from the vehicle moving data and lane data are useful in efficiently analyzing the cause and effect of risky driving behavior.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.1-11
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• 2017

Expressway network which plays an important role on land transportation system, have been developing quantitatively and qualitatively with $7{\times}9$ structure. To cope with complex geometric condition, guidance lane marking has been installed to induce safer lane-changing maneuver. However, there is no standard on guidance lane marking and its effectiveness is also verified with limited scope. The major purpose of this research is to clarify its effectiveness in terms of driving safety aspect using virtual driving simulator and to suggest standard on the proper length. To carry this out, preference data from subjects was collected and lane-changing pattern within virtual driving environment was investigated. In addition, in order to quantify the level of comfort, Electroencephalogram data was collected and validated using statistical test. Finally, it is expected that this research can be used to establish standard on guidance lane marking.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.86-94
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• 2015

In recent days, a Connected Car has caught an attention of the motor companies and various industrial institutes such as communication company. An automobile is regarded as a device and has been developed as an interactive system because the system is connected with various device. This drives a new business item, too. As a new automatic car technology is emerging, a new type of car accident is appeared, too. So, many researches for preventing car accident comes from the driver's are carried out in many car related institutes. In this paper, we study a driver's driving workload and develop an algorithm that measures the driver's driving behavior. We can see that the developed algorithm runs well by the experiment of road test. This results affects various road condition, driver's driving behavior and load that reflects the driver's status.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.167-189
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• 2022

In the case of SAE autonomous driving levels 2 and 3, since complete autonomous driving is impossible, the take-over process is essential, and take-over time(TOT) is the most important factor in determining the safety of the autonomous driving system. Accordingly, research on TOT is being actively conducted, but each research is independently conducted and general conclusions that integrate various research results are required. Therefore, in this study, the factors affecting TOT were analyzed using meta-analysis, which integrates the results of individual studies and presents an integrated opinion. As a result of meta-analysis, a total of 10 influencing factors were selected, and most of them were related to the non-driving related task(NDRT) type. In addition, implications for the future research direction of take-over and NDRT were presented.