Smart transportation systems are pivotal to the evolution of urban mobility, offering efficient, sustainable, and personalized travel experiences. Central to this advancement is the development of personalized path-planning algorithms for autonomous vehicles. This comprehensive survey delves into the state-of-the-art algorithms that enable tailored navigation for users, considering individual preferences and real-time traffic conditions. We examine heuristic approaches, machine learning techniques, and their integration in creating adaptive routes that optimize for time, energy efficiency, and user comfort. Our analysis includes a critical review of the existing literature, highlighting key contributions and identifying potential research gaps. The survey also discusses the practical implications of these algorithms in real-world scenarios, assessing their impact on the efficiency of autonomous vehicle fleets and user satisfaction. By synthesizing current findings and outlining future directions, this paper aims to provide a roadmap for researchers and practitioners in intelligent transportation systems, fostering the development of more sophisticated and user-centered autonomous vehicles


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    Title :

    Smart Transportation: A Comprehensive Survey on Personalized Path Planning Algorithms


    Contributors:
    Saranya, C. (author) / Janaki, G. (author)


    Publication date :

    2024-01-18


    Size :

    940298 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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