Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management
Yıl 2023,
Cilt: 13 Sayı: 1, 105 - 117, 30.06.2023
Abdülkadir Çıldır
,
Mesud Kahriman
,
Mesut Tigdemir
Öz
The main aim of dynamic intersection management is to make instant detection of vehicles both at the intersection and approaching it. In this sense, vehicle detection sensors have been preferred for dynamic intersection management. In this article, a LiDAR sensor system that can detect the number, velocity, and class of vehicles at intersections with different densities and also the length of vehicle queues that may occur at this intersection has been studied. In this study, 96.36 % success has obtained in the detection of the velocity, and 96.35 % success has also obtained in the queue detection. This study, which includes the data taken from a unidimensional LiDAR sensor and the capabilities of a 3D LiDAR sensor, stands out in terms of price and performance.
Kaynakça
-
Akanbi, L., Olajubu, E.A. 2012. A fuzzy-based intelligent traffic
control system for managing VIP-induced chaos at road intersections.
African Journal of Computing ICT 5: 109-119.
-
Ban, X. J., Hao, P., Sun, Z. 2011. Real time queue length estimation
for signalized intersections using travel times from mobile
sensors. Transportation Research Part C: Emerging Technologies
19: 1133-1156.
-
Cai, Y., Zhang, W., Wang H. 2010. Measurement of vehicle
queue length based on video processing in intelligent traffic
signal control system. IEEE. 2010 International Conference
on Measuring Technology and Mechatronics Automation,
615-618, Changsha, China.
-
Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., Wang, L. 2019. Deer
crossing road detection with roadside LiDAR sensor. IEEE
Access 7: 65944-65954.
-
Cheung, S. Y., Coleri, S., Dundar, B., Ganesh, S., Tan, C.-W.,
Varaiya P. 2005. Traffic measurement and vehicle classification
with single magnetic sensor. Transportation research record
1917: 173-181.
-
Cheung, S. Y., Varaiya, P. 2006. Traffic surveillance by wireless
sensor networks. California Path Program Institute Of Transportation
Studies University Of California, Berkeley, Institute
Of Transportation Studies University Of California: 13.
-
Chiu, S., Chand, S. 1993. Adaptive traffic signal control using
fuzzy logic. IEEE. Fuzzy Systems, 1993., Second IEEE International
Conference on, 1371-1376.
-
Choudhary, P. 2018. Analyzing virtual traffic light using state
machine in vehicular ad hoc network. Next-generation networks,
Springer. Delhi, India, 239-245.
-
Cildir, A., Kahriman, M., Tigdemir, M. 2022. The Intersection
Vehicle Delay Optimization For Ideal Traffic Light Cycle
Time. International Journal of 3D Printing Technologies and
Digital Industry 6: 126-136.
-
Emami, A., Sarvi, M., Bagloee, S. A. 2019. A neural network algorithm
for queue length estimation based on the concept of
k-leader connected vehicles. Journal of Modern Transportation
27: 341-354.
-
Festag, A., Noecker, G., Strassberger, M., Lübke, A., Bochow,
B., Torrent-Moreno, M., Schnaufer S., Eigner, R., Catrinescu,
C., Kunisch, J. 2008. “NoW - Network on Wheels”
: Project Objectives, Technology and Achievements. CiteSeerx.
Proceedings of 5rd International Workshop on Intelligent
Transportation (WIT), 211-216, Hamburg, Germany.
-
Goodall, N. J. 2017. Fundamental characteristics of Wi‑Fi and
wireless local area network re-identification for transportation.
IET Intelligent Transport Systems 11: 128-135.
-
Ibisch, A., Stümper, S., Altinger, H., Neuhausen, M.,
Tschentscher, M., Schlipsing, M., Salinen, J., Knoll, A.
2013. Towards autonomous driving in a parking garage: Vehicle
localization and tracking using environment-embedded
lidar sensors. IEEE. 2013 IEEE intelligent vehicles symposium
(IV), 829-834.
-
Lee, H., Coifman, B. 2015. Using LIDAR to validate the performance
of vehicle classification stations. Journal of Intelligent
Transportation Systems 19: 355-369.
-
Liu, H. X., Wu, X., Ma, W., Hu, H. 2009. Real-time queue length
estimation for congested signalized intersections. Transportation
research part C: emerging technologies 17: 412-427.
-
Managuli, M., Deshpande, A., Ayatti, S. H. 2017. Emergent
vehicle tracking system using IR sensor. IEEE. 2017 International
Conference on Electrical, Electronics, Communication,
Computer, and Optimization Techniques (ICEECCOT), 71-
74, Mysuru, India.
-
Margreiter, M. 2016. Fast and Reliable Determination of the
Traffic State Using Bluetooth Detection on German Freeways.
Transportation Research Procedia. World Conference
on Transport Research, Shanghai, China
-
Rani, L. P. J., Kumar, M. K., Naresh, K., Vignesh, S. 2017. Dynamic
traffic management system using infrared (IR) and Internet
of Things (IoT). IEEE. Third International Conference
on Science Technology Engineering & Management (ICONSTEM),
353-357, Chennai, India.
-
Sen, R., Maurya, A., Raman, B., Mehta, R., Kalyanaraman,
R., Vankadhara, N., Roy, S., Sharma, P. 2012. Kyun queue: a
sensor network system to monitor road traffic queues. ACM.
Proceedings of the 10th ACM Conference on Embedded
Network Sensor Systems, 127-140, India.
-
Skoog, W. (1981). “Principles Industrial and Analysis.” 2. Ed.
-
Sparkfun. (2005). “TF03-180 LiDAR(Long-range distance sensor).”
from en.benewake.com.
-
Tiaprasert, K., Zhang, Y., Wang, X. B., Zeng, X. 2015. Queue
length estimation using connected vehicle technology for
adaptive signal control. IEEE Transactions on Intelligent Transportation
Systems 16: 2129-2140.
-
Wu, A., Qi, L., Yang, X. 2013. Mechanism analysis and optimization
of signalized intersection coordinated control under
oversaturated status. 13th COTA International Conference
of Transportation Professionals. Shanghai, China. 96: 1433-
1442.
-
Wu, J., Xu, H., Zhang, Y., Tian, Y., Song, X. 2020. Real-time
queue length detection with roadside LiDAR data. Sensors
20: 2342.
-
Xu, B., Chen, M., Xing, C., Zhang, G. 2009. A network traffic
identification method based on finite state machine. IEEE.
5th International Conference on Wireless Communications,
Networking and Mobile Computing, 1-4, Beijing, China.
-
Yue, X., Wu, B., Seshia, S. A., Keutzer, K., Sangiovanni-Vincentelli,
A. L. 2018. A lidar point cloud generator: from a virtual
world to autonomous driving. Proceedings of the 2018
ACM on International Conference on Multimedia Retrieval,
458-464, Yokohama, Japan.
-
Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D. 2019. Detection
and tracking of pedestrians and vehicles using roadside
LiDAR sensors. Transportation research part C: emerging
technologies 100: 68-87.
-
Zhao, Y., Su, Y. 2017. Vehicles detection in complex urban scenes
using Gaussian mixture model with FMCW radar. IEEE Sensors
Journal 17: 5948-5953.
Dinamik Kavşak Yönetimi İçin Tek-Yönlü LiDAR Sensör ile Yol Yoğunluk Hesabı
Yıl 2023,
Cilt: 13 Sayı: 1, 105 - 117, 30.06.2023
Abdülkadir Çıldır
,
Mesud Kahriman
,
Mesut Tigdemir
Öz
Dinamik kavşak yönetimi, kavşaktaki ve kavşağa yaklaşmakta olan araçların anlık tespit edilmesinden geçmektedir. Bu anlamda dinamik kavşak yönetimi için araç tespit sensörleri tercih sebebi olmuştur. Bu makalede farklı yoğunluklara sahip kavşaklardaki araçların sayısını, hızını, sınıfını ve yine bu kavşakta oluşabilecek olan araç kuyruklarının uzunluğunu tespit edebilen lidar sensörlü bir sistem üzerinde çalışılmıştır. Bu çalışmada hız doğruluk tespitinde 96,36 %, kuyruk uzunluğu tespitinde ise 96,35 % başarı elde edilmiştir. Tek boyutlu bir lidar sensörden alınan veriler ile 3D lidar sensörün yapabileceği kabiliyetleri barındıran bu çalışma, fiyat ve performans açısından öne çıkmaktadır.
Kaynakça
-
Akanbi, L., Olajubu, E.A. 2012. A fuzzy-based intelligent traffic
control system for managing VIP-induced chaos at road intersections.
African Journal of Computing ICT 5: 109-119.
-
Ban, X. J., Hao, P., Sun, Z. 2011. Real time queue length estimation
for signalized intersections using travel times from mobile
sensors. Transportation Research Part C: Emerging Technologies
19: 1133-1156.
-
Cai, Y., Zhang, W., Wang H. 2010. Measurement of vehicle
queue length based on video processing in intelligent traffic
signal control system. IEEE. 2010 International Conference
on Measuring Technology and Mechatronics Automation,
615-618, Changsha, China.
-
Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., Wang, L. 2019. Deer
crossing road detection with roadside LiDAR sensor. IEEE
Access 7: 65944-65954.
-
Cheung, S. Y., Coleri, S., Dundar, B., Ganesh, S., Tan, C.-W.,
Varaiya P. 2005. Traffic measurement and vehicle classification
with single magnetic sensor. Transportation research record
1917: 173-181.
-
Cheung, S. Y., Varaiya, P. 2006. Traffic surveillance by wireless
sensor networks. California Path Program Institute Of Transportation
Studies University Of California, Berkeley, Institute
Of Transportation Studies University Of California: 13.
-
Chiu, S., Chand, S. 1993. Adaptive traffic signal control using
fuzzy logic. IEEE. Fuzzy Systems, 1993., Second IEEE International
Conference on, 1371-1376.
-
Choudhary, P. 2018. Analyzing virtual traffic light using state
machine in vehicular ad hoc network. Next-generation networks,
Springer. Delhi, India, 239-245.
-
Cildir, A., Kahriman, M., Tigdemir, M. 2022. The Intersection
Vehicle Delay Optimization For Ideal Traffic Light Cycle
Time. International Journal of 3D Printing Technologies and
Digital Industry 6: 126-136.
-
Emami, A., Sarvi, M., Bagloee, S. A. 2019. A neural network algorithm
for queue length estimation based on the concept of
k-leader connected vehicles. Journal of Modern Transportation
27: 341-354.
-
Festag, A., Noecker, G., Strassberger, M., Lübke, A., Bochow,
B., Torrent-Moreno, M., Schnaufer S., Eigner, R., Catrinescu,
C., Kunisch, J. 2008. “NoW - Network on Wheels”
: Project Objectives, Technology and Achievements. CiteSeerx.
Proceedings of 5rd International Workshop on Intelligent
Transportation (WIT), 211-216, Hamburg, Germany.
-
Goodall, N. J. 2017. Fundamental characteristics of Wi‑Fi and
wireless local area network re-identification for transportation.
IET Intelligent Transport Systems 11: 128-135.
-
Ibisch, A., Stümper, S., Altinger, H., Neuhausen, M.,
Tschentscher, M., Schlipsing, M., Salinen, J., Knoll, A.
2013. Towards autonomous driving in a parking garage: Vehicle
localization and tracking using environment-embedded
lidar sensors. IEEE. 2013 IEEE intelligent vehicles symposium
(IV), 829-834.
-
Lee, H., Coifman, B. 2015. Using LIDAR to validate the performance
of vehicle classification stations. Journal of Intelligent
Transportation Systems 19: 355-369.
-
Liu, H. X., Wu, X., Ma, W., Hu, H. 2009. Real-time queue length
estimation for congested signalized intersections. Transportation
research part C: emerging technologies 17: 412-427.
-
Managuli, M., Deshpande, A., Ayatti, S. H. 2017. Emergent
vehicle tracking system using IR sensor. IEEE. 2017 International
Conference on Electrical, Electronics, Communication,
Computer, and Optimization Techniques (ICEECCOT), 71-
74, Mysuru, India.
-
Margreiter, M. 2016. Fast and Reliable Determination of the
Traffic State Using Bluetooth Detection on German Freeways.
Transportation Research Procedia. World Conference
on Transport Research, Shanghai, China
-
Rani, L. P. J., Kumar, M. K., Naresh, K., Vignesh, S. 2017. Dynamic
traffic management system using infrared (IR) and Internet
of Things (IoT). IEEE. Third International Conference
on Science Technology Engineering & Management (ICONSTEM),
353-357, Chennai, India.
-
Sen, R., Maurya, A., Raman, B., Mehta, R., Kalyanaraman,
R., Vankadhara, N., Roy, S., Sharma, P. 2012. Kyun queue: a
sensor network system to monitor road traffic queues. ACM.
Proceedings of the 10th ACM Conference on Embedded
Network Sensor Systems, 127-140, India.
-
Skoog, W. (1981). “Principles Industrial and Analysis.” 2. Ed.
-
Sparkfun. (2005). “TF03-180 LiDAR(Long-range distance sensor).”
from en.benewake.com.
-
Tiaprasert, K., Zhang, Y., Wang, X. B., Zeng, X. 2015. Queue
length estimation using connected vehicle technology for
adaptive signal control. IEEE Transactions on Intelligent Transportation
Systems 16: 2129-2140.
-
Wu, A., Qi, L., Yang, X. 2013. Mechanism analysis and optimization
of signalized intersection coordinated control under
oversaturated status. 13th COTA International Conference
of Transportation Professionals. Shanghai, China. 96: 1433-
1442.
-
Wu, J., Xu, H., Zhang, Y., Tian, Y., Song, X. 2020. Real-time
queue length detection with roadside LiDAR data. Sensors
20: 2342.
-
Xu, B., Chen, M., Xing, C., Zhang, G. 2009. A network traffic
identification method based on finite state machine. IEEE.
5th International Conference on Wireless Communications,
Networking and Mobile Computing, 1-4, Beijing, China.
-
Yue, X., Wu, B., Seshia, S. A., Keutzer, K., Sangiovanni-Vincentelli,
A. L. 2018. A lidar point cloud generator: from a virtual
world to autonomous driving. Proceedings of the 2018
ACM on International Conference on Multimedia Retrieval,
458-464, Yokohama, Japan.
-
Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D. 2019. Detection
and tracking of pedestrians and vehicles using roadside
LiDAR sensors. Transportation research part C: emerging
technologies 100: 68-87.
-
Zhao, Y., Su, Y. 2017. Vehicles detection in complex urban scenes
using Gaussian mixture model with FMCW radar. IEEE Sensors
Journal 17: 5948-5953.