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Dr. S. Menaka is Assistant Professor and Head of the Department of Public Administration at Rathinam Global Deemed to be University, Coimbatore, India. Her research interests include public administration, e-governance, digital governance, and smart city development.
The swift progression of Artificial Intelligence (AI) is revolutionizing urban governance, transport management, and the provision of public services. As urban populations expand, municipal officials encounter escalating issues concerning sustainability, transportation, resource distribution, public safety, and civic involvement. AI-driven leadership amalgamates data-informed decision-making, astute governance, predictive analytics, and digital transformation techniques to cultivate innovation and enhance urban living standards. By utilising AI technologies such machine learning, deep learning, Internet of Things (IoT), big data analytics, cloud computing, and digital twins, leaders may optimise urban operations, improve transportation efficiency, and provide citizen-focused public services. This chapter examines the ideas, frameworks, applications, difficulties, and future trajectories of AI-driven leadership in smart cities, transport systems, and public service innovation, highlighting its significance in fostering sustainable, resilient, and intelligent urban ecosystems.
Self-driving vehicles are revolutionising contemporary transit through the incorporation of artificial intelligence, sophisticated sensing technology, and astute decision-making frameworks. Deep learning has become a pivotal technique for allowing autonomous vehicles to sense their environment, identify road participants, analyse traffic situations, foresee possible dangers, and execute secure driving choices. This section explores the core tenets of deep learning in the context of self-driving vehicles, the amalgamation of perceptual and decision-making frameworks, and the use of advanced models for enhancing road safety. It also examines databases, assessment metrics, simulation frameworks, safety verification, ethical implications, and nascent research trajectories. The section presents an extensive framework that integrates multimodal sensor fusion, deep learning-driven risk assessment, and safety-conscious decision-making to enhance the dependability of autonomous vehicles. The discourse emphasises the significance of comprehensive model creation, methodical validation, and human-centric safety protocols for achieving safer and more intelligent transportation systems.