• Journal of Korean Tunnelling and Underground Space Association
• /
• v.11 no.4
• /
• pp.411-417
• /
• 2009

The thermo-flow field in road tunnel is influenced by some facts such as piston effect of vehicle's move, operation of ventilation facilities, natural wind and buoyancy effect of fire plume. Among those, piston effect is one of primary causes for formation of air flow in road tunnel and has an effect on initial direction of smoke flow in tunnel fire. In this study to analyze the unsteady flow in the tunnel caused by the run of vehicle, the experimental study of vehicle-induced unsteady flow on a reduced-scale model tunnel is presented. While the three types of vehicle shape such as basic type of rectangular shape, diamond-head type and stair-tail type are changed, the pressure and air velocity variations with time are measured. The rising ratio of pressure and velocity are in order of "basic type of rectangular shape > stair-tail type > diamond-head type". The experimental results would be good data for development of a numerical method on the vehicle-induced unsteady tunnel flow.

• Korean Journal of Agricultural Science
• /
• v.51 no.2
• /
• pp.133-146
• /
• 2024

Selection of gear reduction ratio is essential for machine design to ensure suitable power and speed during agricultural operations. The goal of the study was to evaluate the gear reduction ratio for a 1.6 kW four-wheel-drive (4WD) multi-purpose agricultural electric vehicle platform using workload data under different off-road conditions. A data acquisition system was fabricated to collect workload (torque) of the vehicle acting on the gear shaft. Field tests were performed under three driving surfaces (asphalt, concrete, and grassland), payload operations (981, 2,942, and 4,903 N), and slope conditions (0 - 4°, 4 - 8°, and 8 - 12°), respectively. Commercial speed reduction gear phases were attached to the input shaft of the vehicle powertrain. The maximum required torque was recorded as 37.5 Nm at a 4,903 N load with 8 - 12° slope levels, and the minimum torque was 12.32 Nm at 0 - 4° slope levels with a 981 Nm load for a 4 km/h speed on asphalt, concrete, and grassland roads. Based on the operating load condition and motor torque and rotational speed (TN) curve, the minimum and maximum gear reduction ratios were chosen as 1 : 50 and 1 : 64, respectively. The selected motor satisfied power requirements by meeting all working torque criteria with the gear reduction ratios. The chosen motor with a gear reduction ratio of 1 : 50 was suitable to fit with the motor T-N curve, and produced the maximum speeds and loads needed for driving and off-road activities. The findings of the study would assist in choosing a suitable gear reduction ratio for electric vehicle multi-purpose field operations.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2012.05a
• /
• pp.281-282
• /
• 2012

• 한국도로학회:학술대회논문집
• /
• 2004.10a
• /
• pp.399-403
• /
• 2004

• IEMEK Journal of Embedded Systems and Applications
• /
• v.11 no.5
• /
• pp.313-323
• /
• 2016

With the development of autonomous vehicle, there has been active research on advanced driver assistance system for road marking detection using vision sensor and 3D Laser scanner. However, vision sensor has the weak points that detection is difficult in situations involving severe illumination variance, such as at night, inside a tunnel or in a shaded area; and that processing time is long because of a large amount of data from both vision sensor and 3D Laser scanner. Accordingly, this paper proposes a road marking detection and classification method using single 2D Laser scanner. This method road marking detection and classification based on accumulation distance data and intensity data acquired through 2D Laser scanner. Experiments using a real autonomous vehicle in a real environment showed that calculation time decreased in comparison with 3D Laser scanner-based method, thus demonstrating the possibility of road marking type classification using single 2D Laser scanner.

• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.779-784
• /
• 2008

This paper presents a the method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. By coherence function method, it is found that the biggest part of the noise source in the road noise is generated by structural vibration on the mechanical-acoustic transfer functions of vehicles. This analysis is modeled as four input/single output system because the noise is generated with four wheels that mechanism of the road noise is very complicated. The coherence function method is proved to be useful tool for identifying of noise source. The overall levels of the interior noise be coherence function method are compared with those measured and calculated by the frequency response function approach using mechanical excitation test. The experimental results have shown a good agreement with the results calculated by the coherence function method when the input sources are coherent strongly each other. The estimation of the road noise indicates that significant coherent can be achieved in the vehicle interior noise.

• ETRI Journal
• /
• v.37 no.5
• /
• pp.1032-1043
• /
• 2015

ETRI's Co-Pilot project is aimed at the development of an automated vehicle that cooperates with a driver and interacts with other vehicles on the road while obeying traffic rules without collisions. This paper presents a core block within the Co-Pilot system; the block is named "Co-Pilot agent" and consists of several main modules, such as road map generation, decision-making, and trajectory generation. The road map generation builds road map data to provide enhanced and detailed map data. The decision-making, designed to serve situation assessment and behavior planning, evaluates a collision risk of traffic situations and determines maneuvers to follow a global path as well as to avoid collisions. The trajectory generation generates a trajectory to achieve the given maneuver by the decision-making module. The system is implemented in an open-source robot operating system to provide a reusable, hardware-independent software platform; it is then tested on a closed road with other vehicles in several scenarios similar to real road environments to verify that it works properly for cooperative driving with a driver and automated driving.

• ETRI Journal
• /
• v.37 no.3
• /
• pp.606-616
• /
• 2015

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.18 no.11
• /
• pp.1206-1212
• /
• 2008

This paper presents a the method for estimating the noise source contribution on the road noise of the vehicle in a multiple input system where the input sources may be coherent with each other. By coherence function method, it is found that the biggest part of the noise source in the road noise is generated by structural vibration on the mechanical-acoustic transfer functions of vehicles. This analysis is modeled as four input/single output system because the noise is generated with four wheels that mechanism of the road noise is very complicated. The coherence function method is proved to be useful tool for identifying of noise source. The overall levels of the interior noise be coherence function method are compared with those measured and calculated by the frequency response function approach using mechanical excitation test. The experimental results have shown a good agreement with the results calculated by the coherence function method when the input sources are coherent strongly each other. The estimation of the road noise indicates that significant coherent can be achieved in the vehicle interior noise.

• Journal of Korean Port Research
• /
• v.13 no.1
• /
• pp.101-112
• /
• 1999

Traffic accidents increase with the increase of the vehicles in operation on the street. Especially big traffic accidents composed of over 3 killed or 20 injured accidents with the property damage become one of the serious problems to be solved in most of the cities. The purpose of this study is to build the discrimination model on big traffic accidents using the Quantification II theory for establishing the countermeasures to reduce the big traffic accidents. The results are summarized as follows. 1)The existing traffic accident related model could not explain the phenomena of the current traffic accident appropriately. 2) Based on the big traffic accident types vehicle-vehicle, vehicle-alone, vehicle-pedestrian and vehicle-train accident rates 73%, 20.5% 5.6% and two cases respectively. Based on the law violation types safety driving non-fulfillment center line invasion excess speed and signal disobedience were 48.8%, 38.1% 2.8% and 2.8% respectively. 3) Based on the law violation types major factors in big traffic accidents were road and environment, human, and vehicle in order. Those factors were vehicle, road and environment, and human in order based on types of injured driver’s death. 4) Based on the law violation types total hitting and correlation rates of the model were 53.57% and 0.97853. Based on the types of injured driver’s death total hitting and correlation rates of the model were also 71.4% and 0.59583.