Smart Robot Design and Implementation to Assist Pedestrian Road Crossing

Abstract:

This research focuses on designing and developing a smart robot to assist pedestrians with road crossings. Pedestrian safety is a major concern, as highlighted by the high annual rates of fatalities and injuries. In 2020, the United States recorded 6,516 pedestrian fatalities and approximately 55,000 injuries, with children under 16 being especially vulnerable. This project aims to address this need by offering an innovative solution that prioritizes real-time detection and intelligent decision-making at inter-sections. Unlike existing studies that rely on traffic light infrastructure, our approach accurately identifies both vehicles and pedestrians at intersections, creating a comprehensive safety system. Our strategy involves implementing advanced Machine Learning (ML) algorithms for real-time detection of vehicles, pedestrians, and cyclists. These algorithms, executed in Python, leverage data from LiDAR and video cameras to assess road conditions and guide pedestrians and cyclists safely through inter-sections. The smart robot, powered by ML insights, will make intelligent decisions to ensure a safer and more secure road-crossing experience for pedestrians and cyclists. This project is a pioneering effort in holistic pedestrian safety, ensuring robust detection capabilities and intelligent decision-making.

Publications:

Smart Robot Design and Implementation to Assist Pedestrian Road Crossing (Full Report)

Research Brief

Authors:

HOVANNES KULHANDJIAN

Dr. Hovannes Kulhandjian is an Associate Professor in the Department of Electrical and Computer Engineering at California State University, Fresno (Fresno State). He joined Fresno State in Fall 2015 as a tenure-track faculty member. Prior to this position, he was an Associate Research Engineer in the Department of Electrical and Computer Engineering at Northeastern University. He received his B.S. degree in Electronics Engineering with high honors from the American University in Cairo (AUC) in 2008, and his M.S. and Ph.D. degrees in Electrical Engineering from the State University of New York at Buffalo in 2010 and 2014, respectively. His current research interests are in applied machine learning, autonomous vehicle navigation, wireless communications, and networking, with applications to underwater and visible light communications and networking geared towards Intelligent Transpiration Systems (ITS).

Dr. Kulhandjian has received six research grants from the Fresno State Transportation Institute (FSTI). He is a recipient of the Claude C. Laval III Award for Commercialization of Research, Innovation and Creativity 2021 as well as the Claude C. Laval Award for Innovative Technology and Research 2020 at Fresno State. In April 2021 as a PI, he received a grant from the Department of Defense (DOD) Research and Education Program for Historically Black Colleges and Universities and Minority-Serving Institutions (HBCU/MI) Equipment/Instrumentation, to establish a “Secure Communications and Embedded Systems Laboratory at California State University, Fresno”.

Dr. Kulhandjian is an active member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is a Senior Member of IEEE. He currently serves as a guest editor for the special issue "Advances in Wireless Sensor Networks and Communication Technology.” He has also served as a Guest Editor for IEEE Access Special Section Journal and for MDPI Special Issue on "Advances in Intelligent Transportation Systems (ITS)", Session Co-Chair for IEEE UComms’20 Conference, Session Chair for ACM WUWNet’19 Conference, Publicity Co-Chair for IEEE BlackSeaCom Conference. He is a member of the Technical Program Committee (TCP) for ACM and IEEE Conferences, such as GLOBECOM 2024, WTS 2024, WD 2021, ICC 2018, WUWNet 2024, and WiMob2019. Dr. Kulhandjian is a recipient of the Outstanding Reviewer Award from ELSEVIER Ad Hoc Networks and ELSEVIER Computer Networks.

Published:

June 2024

Keywords:

Pedestrian detection

Smart robot

Smart traffic light

Safe road crossing

Vehicle detection