Machine Learning for Traffic Management, Object Detection, and Collision Avoidance in Autonomous Driving for Smart City Governance

Abstract

Machine learning (ML) does an excellent job of enhancing traffic management, object detection and collision avoidance in autonomous driving which has direct real-world impact on smart city governance. These algorithms process a vast stream of real-time data coming from sensors, cameras, and IoT devices to facilitate traffic flow by minimizing congestion and optimizing routes. ML in object detection automatically detects pedestrians, vehicles, and obstacles with great accuracy ensuring that safety is increased. ML driven collision avoidance systems prevent accidents by predicting hazards and reacting to potential ones before they happen. When integrated with autonomous driving, ML enables smart cities to create a more level of safety and efficiency in transportation systems that foster sustainable urban mobility. This technology helps to improve the performance of autonomous vehicles and ties in with smart city aims to reduce emissions, energy consumption while improving overall urban life.