AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

Kulhandjian, Hovannes; Barron, Jeremiah; Tamiyasu, Megan; Thompson, Mateo; Kulhandjian, Michel

doi:10.3390/jsan13030034

Open AccessArticle

AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

by

Hovannes Kulhandjian

^1,*

,

Jeremiah Barron

¹,

Megan Tamiyasu

¹,

Mateo Thompson

¹ and

Michel Kulhandjian

²

¹

Department of Electrical and Computer Engineering, California State University, Fresno, CA 93740, USA

²

Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA

^*

Author to whom correspondence should be addressed.

J. Sens. Actuator Netw. 2024, 13(3), 34; https://doi.org/10.3390/jsan13030034

Submission received: 7 April 2024 / Revised: 29 May 2024 / Accepted: 6 June 2024 / Published: 14 June 2024

(This article belongs to the Special Issue Advances in Intelligent Transportation Systems (ITS))

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Abstract

In this paper, we present a pedestrian detection and avoidance scheme utilizing multi-sensor data collection and machine learning for intelligent transportation systems (ITSs). The system integrates a video camera, an infrared (IR) camera, and a micro-Doppler radar for data acquisition and training. A deep convolutional neural network (DCNN) is employed to process RGB and IR images. The RGB dataset comprises 1200 images (600 with pedestrians and 600 without), while the IR dataset includes 1000 images (500 with pedestrians and 500 without), 85% of which were captured at night. Two distinct DCNNs were trained using these datasets, achieving a validation accuracy of 99.6% with the RGB camera and 97.3% with the IR camera. The radar sensor determines the pedestrian’s range and direction of travel. Experimental evaluations conducted in a vehicle demonstrated that the multi-sensor detection scheme effectively triggers a warning signal to a vibrating motor on the steering wheel and displays a warning message on the passenger’s touchscreen computer when a pedestrian is detected in potential danger. This system operates efficiently both during the day and at night.

Keywords: machine learning; pedestrian detection; accident prevention; intelligent transportation systems

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MDPI and ACS Style

Kulhandjian, H.; Barron, J.; Tamiyasu, M.; Thompson, M.; Kulhandjian, M. AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors. J. Sens. Actuator Netw. 2024, 13, 34. https://doi.org/10.3390/jsan13030034

AMA Style

Kulhandjian H, Barron J, Tamiyasu M, Thompson M, Kulhandjian M. AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors. Journal of Sensor and Actuator Networks. 2024; 13(3):34. https://doi.org/10.3390/jsan13030034

Chicago/Turabian Style

Kulhandjian, Hovannes, Jeremiah Barron, Megan Tamiyasu, Mateo Thompson, and Michel Kulhandjian. 2024. "AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors" Journal of Sensor and Actuator Networks 13, no. 3: 34. https://doi.org/10.3390/jsan13030034

APA Style

Kulhandjian, H., Barron, J., Tamiyasu, M., Thompson, M., & Kulhandjian, M. (2024). AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors. Journal of Sensor and Actuator Networks, 13(3), 34. https://doi.org/10.3390/jsan13030034

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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AI-Based Pedestrian Detection and Avoidance at Night Using Multiple Sensors

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