Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{169924,
        author = {Rudra Srivastava and Samyak Jain and Shivansh Shukla and Anchit Dixit},
        title = {Driving Risk Prediction Using Machine Learning: A Comprehensive Study},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2362-2367},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169924},
        abstract = {Driving risk prediction is one of the most critical areas that have been so far researched upon to improvise road safety and optimize autonomous vehicle performance. The paper focuses on several machine learning algorithms that are aimed at predicting driving risks by analysing data regarding the behaviour of drives, vehicle dynamics, and environmental conditions. Thus, both data imbalance and real-time data processing challenges are addressed through advanced predictive modelling techniques. It constructs a general framework, combining feature engineering and model evaluation metrics. The results show that Gradient Boosting and XGBoost are the most promising candidates for the risk assessment level and could enable giant improvements in safety analytics for human-driven and driverless vehicles. These research findings stress the role of data-driven insights in the development of future safety protocols for autonomous vehicles.},
        keywords = {Driving behaviour analysis, machine learning algorithms, predictive modelling, risk assessment, real-time data processing, data imbalance, feature engineering, model evaluation, Gradient Boosting, XGBoost, safety analytics, artificial intelligence in transportation, road safety prediction, telematics data, partial dependence plots, transfer learning, dynamic risk assessment.},
        month = {November},
        }