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@article{198318,
        author = {Vikram Shrivastav and Dr. Shrikant Sonekar and Yash Zade and Dhruv Patel and Prince Singh},
        title = {A Review on Real Time Driver Performance Evaluation and License Issuance using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13204-13209},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198318},
        abstract = {Due to the rising number of road accidents caused by unsafe driving practices, the analysis of driver behavior has emerged as a critical research area within Intelligent Transportation Systems. Recent advancements in Internet of Things (IoT) and machine learning technologies have enabled the development of intelligent driver monitoring systems capable of analyzing real-time behavioral data. This research paper presents a comprehensive review of existing methods for analyzing driver behavior based on IoT sensor data and machine learning techniques. Various methodologies such as sensor-based monitoring systems, machine learning classification models, deep learning methods, and IoT-based architectures are analyzed and compared. This study highlights the strengths and limitations of existing systems and identifies key research gaps, such as the lack of standardized evaluation frameworks and real-time decision-support mechanisms. Finally, future research directions focusing on scalable, real-time, and semantic driver monitoring systems are discussed.},
        keywords = {Driver performance evaluation, Internet of Things, Machine Learning, Random Forest, Driver behavior analysis, Real-time monitoring},
        month = {April},
        }