Driving Risk Prediction Using Machine Learning: A Comprehensive Study

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Rudra Srivastava; Samyak Jain; Shivansh Shukla; Anchit Dixit

Driving Risk Prediction Using Machine Learning: A Comprehensive Study

Authors: Rudra Srivastava, Samyak Jain, Shivansh Shukla, Anchit Dixit

Unique Paper ID: 169924
Volume: 11
Issue: 6
PageNo: 2362-2367

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.

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.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 6
PageNo: 2362-2367

Driving Risk Prediction Using Machine Learning: A Comprehensive Study

Available:https://ijirt.org/article?manuscript=169924

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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