An Analysis of Robotic Dog’s Machine Learning-Based Detection for Pedestrians and Vehicles
M. Hassan Tanveer, Aiden Kovarovics, Charles Koduru, Razvan Cristian Voicu, Carrington Chun, Ghulam Mahdi · 2024
In this paper, we discuss the deployment of an innovative autonomous inspection system to facilitate data collection, specifically focusing on pedestrian and vehicle counts within transportation infrastructure. Utilizing bio-inspired robots, such as a robot dog, we explore new horizons in data gathering that supersede conventional methods. The robot dog, with its ability to navigate multiple terrains, acts as the primary data collection agent, ensuring accuracy and comprehensiveness. These technologies together pave the way for real-time monitoring of pedestrian and vehicle flow, offering a holistic view of transportation usage and patterns. By integrating this data with Intelligent Transportation Systems (ITS), the paper highlights the potential for creating smarter and more efficient transportation networks. This contemporary approach to data collection aligns with the rigorous standards and guidelines set forth by entities like AASHTO, emphasizing the role of automated systems in shaping the future of transportation management and infrastructure monitoring.