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Title	Featuring Smart City Solutions With Machine Learning For Traffic Management, Object Detection, And Collision Avoidance In Autonomous Driving
ID_Doc	26305
Authors	Singh B.
Year	2025
Published	Urban Mobility and Challenges of Intelligent Transportation Systems
DOI	http://dx.doi.org/10.4018/979-8-3693-7984-4.ch019
Abstract	ML algorithms are used in traffic management to analyze real-time data from cameras, sensors and GPS devices, predict congestion, optimize traffic flow and minimize delays. This information allows city planners to adjust traffic signals in real time and prevent congestion, enhancing urban mobility. Machine learning improves the object detection for autonomous driving by training the models to be able to identify vehicles, pedestrians and obstacles accurately, even in complex urban environments. Sophisticated ML-driven systems analyze visual and other sensory data to help the vehicle quickly determine safe actions, if any. Predictive models analyze potential obstacles, allowing vehicles to reduce speed or change route to avoid collisions, thus enhancing collision avoidance capabilities. These machine learning powered systems, work towards the development of safer and optimized urban transportation solutions with smarter/connected cities resulting in improved mobility for all while being efficient-solid. © 2025 by IGI Global Scientific Publishing.
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