• 한국ITS학회:학술대회논문집
• /
• v.2006 no.10
• /
• pp.249-253
• /
• 2006

• 한국ITS학회:학술대회논문집
• /
• v.2007 no.10
• /
• pp.100-104
• /
• 2007

• 한국ITS학회:학술대회논문집
• /
• 2004.11a
• /
• pp.270-275
• /
• 2004

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.11
• /
• pp.181-190
• /
• 2022

In this paper, we use a LiDAR sensor and an image camera to detect a left-turning waiting vehicle in two ways, unlike the existing image-type or loop-type left-turn detection system, and a left-turn traffic signal corresponding to the waiting length of the left-turning lane. A system that can efficiently assign a system is introduced. For the LiDAR signal transmitted and received by the LiDAR sensor, the left-turn waiting vehicle is detected in real time, and the image by the video camera is analyzed in real time or at regular intervals, thereby reducing unnecessary computational processing and enabling real-time sensitive processing. As a result of performing a performance test for 5 hours every day for one week with an intersection simulation using an actual signal processor, a detection rate of 99.9%, which was improved by 3% to 5% compared to the existing method, was recorded. The advantage is that 99.9% of vehicles waiting to turn left are detected by the LiDAR sensor, and even if an intentional omission of detection occurs, an immediate response is possible through self-correction using the video, so the excessive waiting time of vehicles waiting to turn left is controlled by all lanes in the intersection. was able to guide the flow of traffic smoothly. In addition, when applied to an intersection in the outskirts of which left-turning vehicles are rare, service reliability and efficiency can be improved by reducing unnecessary signal costs.

• Journal of KIISE:Software and Applications
• /
• v.31 no.11
• /
• pp.1534-1542
• /
• 2004

A novel vision-based scheme of extracting real-time traffic information parameters is presented. The method is based on a region classification followed by a spatio-temporal image analysis. The detection region images for each traffic lane are classified into one of the three categories: the road, the vehicle, and the shadow, using statistical and structural features. Misclassification in a frame is corrected by using temporally correlated features of vehicles in the spatio-temporal image. Since only local images of detection regions are processed, the real-time operation of more than 30 frames per second is realized without using dedicated parallel processors, while ensuring detection performance robust to the variation of weather conditions, shadows, and traffic load.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.5D
• /
• pp.635-644
• /
• 2009

Image processing technique in the outdoor environment is very sensitive, and it tends to lose a lot of accuracy when it rapidly changes by outdoor environment. Therefore, in order to calculate accurate traffic information using the traffic monitoring system, we must resolve removing shadow in transition time, Distortion by the vehicle headlights at night, noise of rain, snow, and fog, and occlusion. In the research, we developed a system to calibrate the amount of traffic, speed, and time occupancy by using image processing technique in a variety of outdoor environments change. This system were tested under outdoor environments at the Gonjiam test site, which is managed by Korea Institute of Construction Technology (www.kict.re.kr) for testing performance. We evaluated the performance of traffic information, volume counts, speed, and occupancy time, with 4 lanes (2 lanes are upstream and the rests are downstream) from the 16th to 18th December, 2008. The evaluation method performed as based on the standard data is a radar detection compared to calculated data using image processing technique. The System evaluation results showed that the amount of traffic, speed, and time occupancy in period (day, night, sunrise, sunset) are approximately 92-97% accuracy when these data compared to the standard data.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.19 no.1
• /
• pp.121-131
• /
• 2020

Current traffic information is collected through a loop detector or an image detector. This method is influenced by weather and time, so a traffic information generation system is needed to replace it. A system for generating traffic information using a smartphone in a vehicle is proposed and the performance of the proposed method is verified through the collection rate and the travel time error rate obtained through field tests. In addition, we propose an algorithm for generating intersection traffic information for each direction of rotation, suggest ways to increase the amount of valid information, and confirm the results.

• Journal of Korean Society of Transportation
• /
• v.20 no.2
• /
• pp.135-148
• /
• 2002

In this paper, a queue length calculation algorithm using image detectors has been proposed. The algorithm produces the queue length using a pair of image detectors installed both on upstream and on downstream of a corridor. In addition, a new framework for controlling the traffic signal system based on queue length has been presented. More specifically, the scheme of determining the cycle time and green split using the queue lengths has been proposed. To validate the results, a simulation study was conducted with a network environment. Results showed that the proposed method gave better operational performance than a traditional method. However, additional validation effort is necessary in order to apply the real traffic conditions.

• Journal of Korean Society of Transportation
• /
• v.27 no.5
• /
• pp.97-111
• /
• 2009

Image-based traffic information collection systems have entered widespread adoption and use in many countries since these systems are not only capable of replacing existing loop-based detectors which have limitations in management and administration, but are also capable of providing and managing a wide variety of traffic related information. In addition, these systems are expanding rapidly in terms of purpose and scope of use. Currently, the utilization of image processing technology in the field of traffic accident management is limited to installing surveillance cameras on locations where traffic accidents are expected to occur and digitalizing of recorded data. Accurately recording the sequence of situations around a traffic accident in a signal intersection and then objectively and clearly analyzing how such accident occurred is more urgent and important than anything else in resolving a traffic accident. Therefore, in this research, we intend to present a technology capable of overcoming problems in which advanced existing technologies exhibited limitations in handling real-time due to large data capacity such as object separation of vehicles and tracking, which pose difficulties due to environmental diversities and changes at a signal intersection with complex traffic situations, as pointed out by many past researches while presenting and implementing an active and environmentally adaptive methodology capable of effectively reducing false detection situations which frequently occur even with the Gaussian complex model analytical method which has been considered the best among well-known environmental obstacle reduction methods. To prove that the technology developed by this research has performance advantage over existing automatic traffic accident recording systems, a test was performed by entering image data from an actually operating crossroad online in real-time. The test results were compared with the performance of other existing technologies.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.12 no.4
• /
• pp.11-21
• /
• 2013

The conventional real-time signal control strategy for oversaturated situation generally requires a number of detectors at the intersection in order to identify the queue length at each approach. Also, existing strategy for the spillback has limited effect due to the temporal spillback control which only reduce the green split at the approach. In this study, a signal control system utilizing the imagery information from ITS roadside equipment is developed for operation of oversaturated intersections. The strategy calculates the saturation ratio based on the queue length extracted from ITS RSE, and designs the signal control variables according to the saturation ratio. The signal control strategy is divided into two phases: oversaturated and supersaturated. In oversaturated conditions, timing plan for main approach is optimized by the queue length. In oversaturated conditions where spillback might occur, the signal timing is designed in order to avoid the spillback. To increase field adaptability, the strategy is designed to adjust the split length, all-red-time, and clearance time, and keep the major signal control variables intact. The result of the simulation shows that in oversaturated conditions, the improvement is similar to the real-time signal control system. In case of, oversaturated conditions, however, the effect of the strategy is superior to that of a real-time system.