• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.5
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• pp.38-48
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• 2007

In ubiquitous environment, sensor networks that sense and transmit surrounding data without human intervention will become more important. If sensors are installed for detecting vehicles and measuring their speed in the road and that real-time information is given to drivers, it will be very effective for enhancing safety and controlling traffic in the road. In this paper, we proposed a new reliable and real-time sensor MAC protocol between AP and sensor nodes in order to provide real-time traffic flow information based on ubiquitous sensor networks. The proposed MAC allocates one TDMA slot for each sensor node on the IEEE 802.15.4 based channel structure, introduces relayed communication for distant sensors, and adopts a frame structure that supports retransmission for the case of errors. In addition, the proposed MAC synchronizes with AP by using beacon and adopts a hybrid tracking mode that supports economic power consumption according to various traffic situations, We implemented a simulator for the proposed MAC by using sim++ and evaluated various performances. The simulation results show that the proposed MAC reduces the power consumption and reveals excellent performance in real-time application systems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.323-330
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• 2020

This paper proposes the road roughness based Braking Pressure Calculation System(BPCS) for an Autonomous Vehicle Stability. The system consists of an image normalization module that processes the front image of a vehicle to fit the input of the random forest, a Random Forest based Road Roughness Classification Module that distinguish the roughness of the road on which the vehicle is travelling by using the weather information and the front image of a vehicle as an input, and a brake pressure control module that modifies a friction coefficient applied to the vehicle according to the road roughness and determines the braking strength to maintain optimal driving according to a vehicle ahead. To verify the efficiency of the BPCS experiment was conducted with a random forest model. The result of the experiment shows that the accuracy of the random forest model was about 2% higher than that of the SVM, and that 7 features should be bagged to make an accurate random forest model. Therefore, the BPCS satisfies both real-time and accuracy in situations where the vehicle needs to brake.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.162-177
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• 2020

The perception of traffic environment based on various sensors in autonomous driving system has a direct relationship with driving safety. Recently, as the perception model based on deep neural network is used due to the development of machine learning/in-depth neural network technology, a the perception model training and high quality of a training dataset are required. However, there are several realistic difficulties to collect data on all situations that may occur in self-driving. The performance of the perception model may be deteriorated due to the difference between the overseas and domestic traffic environments, and data on bad weather where the sensors can not operate normally can not guarantee the qualitative part. Therefore, it is necessary to build a virtual road environment in the simulator rather than the actual road to collect the traning data. In this paper, a training dataset collection process is suggested by diversifying the weather, illumination, sensor position, type and counts of vehicles in the simulator environment that simulates the domestic road situation according to the domestic situation. In order to achieve better performance, the authors changed the domain of image to be closer to due diligence and diversified. And the performance evaluation was conducted on the test data collected in the actual road environment, and the performance was similar to that of the model learned only by the actual environmental data.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.135-142
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• 2022

In this paper, we propose a graph-based user route control for rapidly conducting virus inspections in emergency situations (eg, COVID-19) and a framework that can simulate this on a city map. A^* and navigation mesh data structures, which are widely used pathfinding algorithms in virtual environments, are effective when applied to CS(Computer science) problems that control Agents in virtual environments because they guide only a fixed static movement path. However, it is not enough to solve the problem by applying it to the real COVID-19 environment. In particular, there are many situations to consider, such as the actual road traffic situation, the size of the hospital, the number of patients moved, and the patient processing time, rather than using only a short distance to receive a fast virus inspection.

• Journal of Korean Society for Atmospheric Environment
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• v.29 no.6
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• pp.745-756
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• 2013

The purpose of this study is to evaluate representativeness of fuel-based emission factors. Twelve light-duty diesel vehicles which meet Euro-3 to 5 legislative emission limits were selected for emission tests. Second-by-second modal emission rates of vehicles were measured on a standard laboratory chassis dynamometer system. An off-cycle driving cycle was developed as a representative Korean real-world on-road driving cycle. Fuel-based emission factors were developed for short trip segments that involved in the selected driving cycle. Each segment was defined to have unit travel distance, which is 1 km, and characterized by its average speed and Relative Positive Acceleration (RPA). Fuel-based $NO_x$ emission factors demonstrate relatively good representativeness in terms of vehicle operation conditions. $NO_x$ emission factors are estimated to be within ${\pm}20%$ of area-wide emission factor under more than 40% of total driving situations. This result implies that the fuel-based $NO_x$ emission factor could be practically implemented into the on-road emission management strategies, such as a remote sensing device (RSD). High emitting vehicles as well as high emitting operating conditions heavily affect on the mean values and distributions of CO and THC emission factors. Few high emitting conditions are pulling up the mean value and biasing the distributions, which weaken representativeness of fuel-based CO and THC emission factors.

• Smart Media Journal
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• v.6 no.3
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• pp.15-20
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• 2017

Recently, the automotive industry has been studied about driver assistance systems for helping drivers to drive their cars easily by integrating them with the IT technology. This study suggests a method of detecting lanes, robust to road condition changes and applicable to lane departure warning and autonomous vehicles mode. The proposed method uses the method of detecting candidate areas by using the Gaussian filter and by determining the Otsu threshold value and edge. Moreover, the proposed method uses lane gradient and width information through the Hough transform to detect lanes. The method uses road lane information detected before to detect dashed lines as well as solid lines, calculates routes in which the lanes will be located in the next frame to draw virtual lanes. The proposed algorithm was identified to be able to detect lanes in both dashed- and solid-line situations, and implement real-time processing where applied to Raspberry Pi 2 which is open source hardware.

• Journal of Mechanical Science and Technology
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• v.20 no.1
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• pp.29-41
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• 2006

Nowadays, with the advancement of computers, computer simulation linked with VR (Virtual Reality) technology has become a useful method for designing the automotive driving system. In this paper, the VR simulation system was developed to investigate the driving performances of the ASV (Advanced Safety Vehicle) equipped with an ACC (Adaptive Cruise Control) system. For this purpose, VR environment which generates visual and sound information of the vehicle, road, facilities, and terrain was organized for the realistic driving situation. Mathematical models of vehicle dynamic analysis, which includes the ACC algorithm, have been constructed for computer simulation. The ACC algorithm modulates the throttle and the brake functions of vehicles to regulate their speeds so that the vehicles can keep proper spacing. Also, the real-time simulation algorithm synchronizes vehicle dynamics simulation with VR rendering. With the developed VR simulation system, several scenarios are applied to evaluate the adaptive cruise controlled vehicle for various driving situations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.234-240
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• 2003

This study aims to investigate dynamic properties of express buses in the very low frequencies which affect motion sickness incidence. Since passengers often use express buses for long distance traveling, it is a critical point whether a give rise to motion sickness or not. In the study accelerations at the three points on the floor of the six test vehicles were measured during the driving at constant speeds. By applying frequency weighting curves suggested in ISO 2631-1 and ISO 2631-3, physical amount of accelerations were changed into perceptual amount which determines incidence of motion sickness. Motion sickness dose values were calculated from the frequency weighted time history of accelerations, and compared between the vehicles, driving conditions, and the seat positions in the bus. During the driving on public road and high ways for 50 minutes vomiting incidence ratios ranged 0.4 to 0.8%, which were equivalent to 2.4 to 4.8% for 5 hours' driving. The value of 4.8 % means two among 45 passengers may vomit after the traveling, which is very serious situation. Considering the very smooth driving condition at which the data were collected, motion sickness dose values will increase in real situations

• Advances in environmental research
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• v.11 no.1
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• pp.17-30
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• 2022

Road transport allows all forms of land conditions to be met at less cost. Because of this function, despite numerous disadvantages, it becomes the most frequently used method of transport, especially in underdeveloped or developing countries. One of the most significant factors used in evaluating the atmosphere's air quality is the amount of CO₂, increasing people's density in indoor spaces. The amount of CO₂ indoors is, therefore, vital to determine. In this study, CO₂ and temperature measurements made on nine different bus journey was made in Turkey. The minimum and maximum values were recorded as 555 ppm and 3000 ppm CO₂, respectively, in the measurements. On all journeys, the average concentration is 1088.72 ppm. The minimum and maximum values were measured as 17.4℃ and 32.7℃ in the temperature measurements, and the average of all trips was calculated to be 25.76℃. In this study conducted before the Covid-19 pandemic, it was determined that the amount of CO₂ increased with the density and insufficient ventilation in the buses. The risk of infection increases in places with high human density and low clean air. For situations such as pandemics, CO₂ measurement is a rapid indicator of determining human density.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.106-116
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• 2024

Ensuring robust 3D object detection is a core challenge for autonomous driving systems operating in urban environments. To tackle this issue, various 3D representation, including point cloud, voxels, and pillars, have been widely adopted, making use of LiDAR, Camera, and Radar sensors. These representations improved 3D object detection performance, but real-world urban scenarios with unexpected situations can still lead to numerous false positives, posing a challenge for robust 3D models. This paper presents a post-processing algorithm that dynamically adjusts object detection thresholds based on the distance from the ego-vehicle. While conventional perception algorithms typically employ a single threshold in post-processing, 3D models perform well in detecting nearby objects but may exhibit suboptimal performance for distant ones. The proposed algorithm tackles this issue by employing adaptive thresholds based on the distance from the ego-vehicle, minimizing false negatives and reducing false positives in the 3D model. The results show performance enhancements in the 3D model across a range of scenarios, encompassing not only typical urban road conditions but also scenarios involving adverse weather conditions.