Thursday 23 January 2025
Driving habits can be a major factor in road safety, and researchers are working on ways to identify and classify different driving styles. A new study has developed an algorithm that uses machine learning techniques to detect three distinct driving behaviors: drowsy, normal, and aggressive.
The algorithm is based on a spatial treatment of the inputs and outputs, using principal component analysis (PCA) to reduce the dimensionality of the data. The system is then trained using fuzzy inference systems (FIS), which are able to model complex relationships between variables. The output of the FIS is then raised to different powers to create three distinct classes: drowsy, normal, and aggressive.
The algorithm was tested on real-world driving data from six drivers, with a total distance of over 1,000 kilometers. The results showed that the system was able to accurately classify driving behavior in over 90% of cases, with an average accuracy of 92%. The system also performed well in identifying unusual or abnormal driving patterns.
One of the key challenges in developing this algorithm was dealing with the complexity and variability of real-world driving data. The researchers used a combination of machine learning techniques, including PCA and FIS, to reduce the dimensionality of the data and create a robust and accurate classification system.
The potential applications of this technology are significant. For example, it could be used to develop personalized driver coaching systems that provide feedback to drivers on their behavior and help them improve their skills. It could also be used to identify high-risk drivers and target them with safety campaigns or other interventions.
In addition, the algorithm could be used in autonomous vehicles to improve their ability to respond to different driving scenarios and environments. By understanding the behavior of human drivers, autonomous vehicles can better anticipate and respond to unexpected events and road conditions.
Overall, this study demonstrates the potential of machine learning techniques to analyze and classify complex data sets, such as those generated by real-world driving. The results have significant implications for the development of advanced driver assistance systems and autonomous vehicles, and highlight the importance of understanding human behavior in designing these technologies.
Cite this article: “Machine Learning Algorithm Accurately Classifies Driving Behaviors”, The Science Archive, 2025.
Machine Learning, Driving Habits, Road Safety, Algorithm, Pca, Fis, Fuzzy Inference Systems, Real-World Data, Autonomous Vehicles, Driver Coaching.
