Smart Streetlight Energy Saving System Based on mmWave Radar


  • Yu-Kai Lin Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan, ROC
  • Jhih-Ci Li Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan, ROC
  • Kai-Li Wang Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan, ROC
  • Yu-Ping Liao Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan, Taiwan, ROC



deep learning, LSTM, mmWave radar, point cloud data, DBSCAN


Streetlights serve as fundamental infrastructure to meet the lighting needs of people on every road. However, their extensive deployment often results in unnecessary energy waste, with many streetlights maintaining high brightness despite minimal usage during the night. This study aims to develop a smart energy-efficient streetlight system that automatically adjusts lighting levels based on the absence of vehicles and pedestrians, detected after a 3-minute countdown. Specifically, the study utilizes mmWave radar to collect point cloud data, which undergoes denoising through Doppler, DBSCAN, and XYZ techniques. Additionally, the mmWave radar assists in training an LSTM model to identify pedestrian pathways. The implementation of the proposed system significantly reduces energy consumption and annual costs by automatically dimming or turning off streetlights in areas with minimal pedestrian activity during nighttime.


G. Allen, “The Private Finance Initiative (PFI),” Economic Policy and Statistics Section, House of Commons Library, Research Paper 03/79, October 21, 2003.

J. W. Selsky and B. Parker, “Cross-Sector Partnerships to Address Social Issues: Challenges to Theory and Practice,” Journal of Management, vol. 31, no. 6, pp. 849-873, December 2005.

S. Gupta, P. K. Rai, A. Kumar, P. K. Yalavarthy, and L. R. Cenkeramaddi, “Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images,” IEEE Sensors Journal, vol. 21, no. 18, pp. 19993-20001, September 2021.

Y. P. Liao, F. K. Huang, Y. J. Xia, and H. Cheng, “Smart Speaker Based on Detection of Millimeter Wave,” IEEE International Conference on Consumer Electronics-Taiwan, pp. 439-440, July 2022.

Joybien Technologies Company Limited, DatasheetBM501MAC, 2022.

R. Zhang and S. Cao, “Real-Time Human Motion Behavior Detection via CNN Using mmWave Radar,” IEEE Sensors Letters, vol. 3, no. 2, article no. 3500104, February 2019.

J. Jung, S. Lim, B. K. Kim, and S. Lee, “CNN-Based Driver Monitoring Using Millimeter-Wave Radar Sensor,” IEEE Sensors Letters, vol. 5, no. 3, article no. 3500404, March 2021.

X. Fan, C. Chen, Z. Huang, L. Tang, J. He, and Y. Jia, “Hand-gesture Recognition Based on Parallelism CNN and Multi-domain Representation for mmWave Radar,” 8th International Conference on Signal and Image Processing, pp. 693-697, July 2023.

J. Horne, “mmWave Radar, You Won’t See It Coming,”, February 01, 2022.

C. Iovescu and S. Rao, “The Fundamentals of Millimeter Wave Radar Sensors,” Texas Instruments, 2017.

D. Salami, R. Hasibi, S. Palipana, P. Popovski, T. Michoel, and S. Sigg, “Tesla-Rapture: A Lightweight Gesture Recognition System from mmWave Radar Sparse Point Clouds,” IEEE Transactions on Mobile Computing, vol. 22, no. 8, pp. 4946-4960, August 2023.

B. Zhang, G. Xu, R. Zhou, H. Zhang, and W. Hong, “Multi-Channel Back-Projection Algorithm for mmWave Automotive MIMO SAR Imaging with Doppler-Division Multiplexing,” IEEE Journal of Selected Topics in Signal Processing, vol. 17, no. 2, pp. 445-457, March 2023.

M. Wang, F. Wang, C. Liu, M. Ai, G. Yan, and Q. Fu, “DBSCAN Clustering Algorithm of Millimeter Wave Radar Based on Multi Frame Joint,” 4th International Conference on Intelligent Control, Measurement and Signal Processing, pp. 1049-1053, July 2022.

S. Palipana, D. Salami, L. A. Leiva, and S. Sigg, “Pantomime: Mid-Air Gesture Recognition with Sparse Millimeter-Wave Radar Point Clouds,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 5, no. 1, article no. 27, March 2021.

E. Stevens, L. Antiga, and T. Viehmann, Deep Learning with PyTorch, New York: Manning Publications, 2020.

T. M. Breuel, “High Performance Text Recognition Using a Hybrid Convolutional-LSTM Implementation,” 14th IAPR International Conference on Document Analysis and Recognition, pp. 11-16, November 2017.

Y. J. Guo and Y. H. Chen, “Statistics and Analysis of Energy-Saving Application in LED Street Lighting for Newly Constructed Roads in Kaohsiung City,” Construction Office Public Works Bureau Kaohsiung City Government, June 30, 2015. (In Chinese)




How to Cite

Yu-Kai Lin, Jhih-Ci Li, Kai-Li Wang, and Yu-Ping Liao, “Smart Streetlight Energy Saving System Based on mmWave Radar”, Adv. technol. innov., vol. 9, no. 2, pp. 116–128, Apr. 2024.