CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Adaptive Headlight Control and Real-Time Pedestrian Detection

by Mohammed Zakaria, Esraa Yaser, Mohammed Rooby, Mohammed Hamed, Mohammed Mahmoud, Azza M. Anis
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 34
Year of Publication: 2024
Authors: Mohammed Zakaria, Esraa Yaser, Mohammed Rooby, Mohammed Hamed, Mohammed Mahmoud, Azza M. Anis
10.5120/ijca2024923902

Mohammed Zakaria, Esraa Yaser, Mohammed Rooby, Mohammed Hamed, Mohammed Mahmoud, Azza M. Anis . Adaptive Headlight Control and Real-Time Pedestrian Detection. International Journal of Computer Applications. 186, 34 ( Aug 2024), 18-25. DOI=10.5120/ijca2024923902

@article{ 10.5120/ijca2024923902,
author = { Mohammed Zakaria, Esraa Yaser, Mohammed Rooby, Mohammed Hamed, Mohammed Mahmoud, Azza M. Anis },
title = { Adaptive Headlight Control and Real-Time Pedestrian Detection },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2024 },
volume = { 186 },
number = { 34 },
month = { Aug },
year = { 2024 },
issn = { 0975-8887 },
pages = { 18-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number34/adaptive-headlight-control-and-real-time-pedestrian-detection/ },
doi = { 10.5120/ijca2024923902 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-08-26T20:51:36.501539+05:30
%A Mohammed Zakaria
%A Esraa Yaser
%A Mohammed Rooby
%A Mohammed Hamed
%A Mohammed Mahmoud
%A Azza M. Anis
%T Adaptive Headlight Control and Real-Time Pedestrian Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 34
%P 18-25
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In rural areas, illumination is weak due to a lack of ambient light, and the risk of being in a fatal crash is higher at night compared to the day. Besides, headlamps are not properly utilized because drivers ignore oncoming vehicles and keep on utilizing their high beams that induce glare for approaching cars, resulting in temporary blindness. Therefore, having a clear view of the road and nighttime object recognition are critical in this situation. This paper introduces the design of an adaptive headlight control system for automobiles with an artificial intelligence object detection module to detect objects in front of a car. The system includes a camera-radar sensor fusion model to evaluate the sensor's readings and fuse them into one readout for a microcontroller unit (MCU). Then, the MCU controls servo motors mounted to light emitting diode (LED) arrays and rotates them right or left according to the steering wheel sensor angle. Hence, the intensity of the headlight LEDs is adapted to reduce the glare on oncoming vehicles and enhance vision at night. The reliability of the designed system is verified by Proteus. Then, a prototype for the proposed design is implemented and tested at different road scenarios.

References
  1. World health organization: road traffic injuries. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
  2. Youn, J., Di-Yin, M., Cho, J., and Park, D. 2015. Steering wheel-based adaptive headlight controller with symmetric angle sensor compensator for functional safety requirement. In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE). IEEE.‏ 619-620).
  3. Xiong, S., Tan, G., Guo, X., and Xiao, L. 2017. The research of the adaptive front lighting system based on GIS and GPS. SAE Technical Paper.‏
  4. Ahmed, I. 2020. Adaptive light control systems. Technical Report.
  5. Li, G., Yang, Y., and Qu, X. 2019. Deep learning approaches on pedestrian detection in hazy weather. IEEE Transactions on Industrial Electronics. 67(10), 8889-8899.‏
  6. Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., and Farhan, L. 2021. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 1-74.‏
  7. Saraf, P., Watve, S., and Kulkarni, A. 2022. Object detection: literature review. In International Conference on Soft Computing and Pattern Recognition. Cham: Springer Nature Switzerland.‏ 485-494.
  8. Terven, J., Córdova-Esparza, D. M., and Romero-González, J. A. 2023. A comprehensive review of yolo architectures in computer vision: from yolov1 to yolov8 and yolo-nas. Machine Learning and Knowledge Extraction. 5(4), 1680-1716.‏
  9. Roboflow dataset. https://roboflow.com‏
  10. Yeong, D. J., Velasco-Hernandez, G., Barry, J., and Walsh, J. 2021. Sensor and sensor fusion technology in autonomous vehicles: a review. Sensors. 21(6), 2140.‏
  11. Novak, D., and Riener, R. 2015. A survey of sensor fusion methods in wearable robotics. Robotics and Autonomous Systems. 73, 155-170.‏
  12. Mendes-Jr, J. J. A., Vieira, M. E. M., Pires, M. B., and Stevan-Jr, S. L. 2016. Sensor fusion and smart sensor in sports and biomedical applications. Sensors. 16(10), 1569.‏‏
  13. Fung, M. L., Chen, M. Z., and Chen, Y. H. 2017. Sensor fusion: a review of methods and applications. In 2017 29th Chinese Control and Decision Conference (CCDC). IEEE.‏ 3853-3860.
  14. Sengupta, A., Yoshizawa, A., and Cao, S. 2022. Automatic radar-camera dataset generation for sensor-fusion applications. IEEE Robotics and Automation Letters. 7(2), 2875-2882.
  15. Tyagi, N., Chavan, S. S., and Gangadharan, S. M. P. 2023. Towards intelligent industrial systems: a comprehensive survey of sensor fusion techniques in IIoT. Measurement: Sensors. 100944.
  16. Abdelkader, M. R., Abdullah, E. T., Mohamed, R. A., Salam, R. K., Mohamed, O. Y., and Anis, A. M. 2023. AStar-algorithm based voice-controlled wheelchair for quadriplegic patients. International Journal of Computer Applications. 185(34), 31-35.
  17. Khodarahmi, M., and Maihami, V. 2023. A review on Kalman filter models. Archives of Computational Methods in Engineering. 30(1), 727-747.‏
  18. Liu, H., Hu, F., Su, J., Wei, X., and Qin, R. 2020. Comparisons on Kalman-filter-based dynamic state estimation algorithms of power systems. IEEE Access. 8, 51035-51043.‏
  19. Zhong, C., Darbandi, M., Nassr, M., Latifian, A., Hosseinzadeh, M., and Navimipour, N. J. 2024. A new cloud-based method for composition of healthcare services using deep reinforcement learning and Kalman filtering. Computers in Biology and Medicine. 172, 108152.‏
  20. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. 2017. CARLA: an open urban driving simulator. In Conference on Robot Learning. PMLR.‏ 1-16.
  21. Unreal engine. https://www.unrealengine.com
  22. Python API tutorial - CARLA simulator. https://carla.readthedocs.io/en/0.9.2/python_api_tutorial
  23. Gao, W., Tang, J., and Wang, T. 2021. An object detection research method based on CARLA simulation. In Journal of Physics: Conference Series. IOP Publishing.‏ 1948(1), 012163.
  24. Proteus: PCB design and circuit simulator software. https://www.labcenter.com
  25. Raspberry Pi foundation. https://www.raspberrypi.org
  26. Blender software. https://www.blender.org
Index Terms

Computer Science
Information Sciences

Keywords

Adaptive Headlight System CARLA CNN Deep Learning Object Detection Roboflow Dataset YOLO v8 Sensor Fusion Raspberry Pi.

Powered by PhDFocusTM