Mohammad Sajid Shahriar

ORCID: 0000-0003-3129-9466
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About
Contact & Profiles
Research Areas
  • Traffic control and management
  • Vehicular Ad Hoc Networks (VANETs)
  • Hand Gesture Recognition Systems
  • Traffic Prediction and Management Techniques
  • Autonomous Vehicle Technology and Safety
  • Robotics and Automated Systems
  • Smart Parking Systems Research
  • Software-Defined Networks and 5G
  • Human Pose and Action Recognition
  • Network Traffic and Congestion Control
  • Transportation Planning and Optimization
  • Hearing Impairment and Communication
  • Transportation and Mobility Innovations
  • Advanced Optical Network Technologies

North Carolina State University
2023-2024

Inha University
2023

International University of Business Agriculture and Technology
2021

In recent years, improving intersection traffic safety has become a major focus for researchers. However, it remains significant concern due to the increasing number of vehicles and introduction autonomous cooperative driving systems. Advancements in artificial intelligence (AI) vehicle-to-everything (V2X) technologies offer promising solutions reduce collisions between vehicles. As V2X are slowly being integrated into systems, questions arise about their impact on effectiveness. This...

10.1109/access.2023.3319382 article EN cc-by-nc-nd IEEE Access 2023-01-01

Recent research on reinforcement learning (RL) based traffic management shows promising results, yet it is a significant issue due to increasing volume of and lack real time information. Improvements RL algorithms vehicle-to-everything (V2X) communications technologies are creating new prospects achieve better efficiency. This paper proposes method, namely Vehicle-to-Infrastructure Traffic Signal Control (V2I-TSC), capture realistic state using vehicle-to-infrastructure (V2I) under 5G-NR-V2X...

10.1016/j.icte.2023.08.002 article EN cc-by-nc-nd ICT Express 2023-08-16

Fast, accurate, and user-friendly human-computer interaction (HCI) requires both processing intelligence. Understanding signs, symbols is already possible by computers but recognizing drawn live a human in front of camera still new concept. Many attempts have been made to achieve this using different sensors like Time Flight (ToF) camera, Kinect sensor, etc., special metric systems or algorithms. Our research proposes doing such work normal cameras that almost every computer has already. In...

10.1109/acmi53878.2021.9528150 article EN 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) 2021-07-08

Autonomous Valet Parking (AVP) is a technology that enables vehicles to park themselves without human intervention. It uses advanced sensing and communication systems find suitable parking space the vehicle safely efficiently. While various artificial intelligence (AI) based methods have demonstrated benefits of AVP, including reducing traffic congestion, improving safety, enhancing convenience comfort for drivers, issue developing evaluating AVP can effectively handle multi-zone areas in...

10.1109/access.2023.3307571 article EN cc-by-nc-nd IEEE Access 2023-01-01

This letter indicates the critical need for prioritized multi-tenant quality-of-service (QoS) management by emerging mobile edge systems, particularly high-throughput beyond fifth-generation networks. Existing traffic engineering tools utilize complex functions baked into closed, proprietary infrastructures, largely limiting design flexibility, scalability, and adaptiveness. Hence, this study introduces a software-defined networking (SDN)-based dynamic QoS provisioning scheme that...

10.48550/arxiv.2403.15975 preprint EN arXiv (Cornell University) 2024-03-23

In the realms of internet vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, increasing number connected autonomous (CAVs) EC-based applications presents multi-domain challenges such inefficient flow due to poor CAV coordination flow-table overflow in SDVN from increased connectivity limited ternary content addressable memory (TCAM)...

10.1109/vtc2024-fall63153.2024.10757450 article EN 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall) 2024-10-07

Over the past several years, there has been a growing focus on research aimed at decreasing rising number of pedestrian fatalities in traffic accidents. Advances Vehicle-to-Pedestrian (V2P) communications have opened up new possibilities for improving safety, but these potential solutions yet to be thoroughly explored. This paper outlines cooperative safety framework (CPSF) non-signalized crossings that leverages 5G-NR V2P warn both pedestrians and drivers. The also considers limited battery...

10.1109/icufn57995.2023.10199945 article EN 2023-07-04
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