Real time analysis of driving data is quickly emerging in connected cars due to machine learning technology in the field. With the help of information collected, sophisticated algorithms, connected cars can guess driving patterns enhancing safety and performance on the roads. Using patterns involve speed, braking and the lane changes, the machine learning system enables the car to develop the capabilities of adapting to the different driving conditions and even a prediction of the likely dangers. This integration of technology improves the decision-making functionality of cars and paves the way toward better levels of autonomy in vehicles. The aim of the paper is to examine the contribution of the emerging technologies, and in particular machine learning technologies, to the analysis of driving data and forecasting of driving behavior. Machine learning becomes even more important in the improvement of safety, efficiency and the overall experience, since the automotive industry is embracing connected car technologies which allows the exploitation of huge databases. The research process is gathering data through vehicle telematics and employs machine learning algorithms such as regression and neural networks to make sense of the driving activity and system performance. Machine learning approaches were found to be substantially more effective than conventional forecasting approaches in prediction of driving behavior. Results indicate the availability of some set pattern within the driving data that is linked to military safety events, and at the same time these patterns can be of great use for vehicle manufacturers and policy-makers concerned with vehicle safety enhancement. Besides, the paper underscores the relevance of the data about the potential user of the model and of the real-time data processing in order to adjust and refine the predictive model. This research has helped in the advancement of the automotive sector as well as future research. Combining machine learning with connected car technologies enables makers of vehicles to construct intelligent cars, which can enhance the safety of driving while at the same time fulfilling the needs of the drivers.


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    Title :

    Machine Learning Technology Assists Connected Cars in Driving Data and Predicting Driving Behaviour


    Contributors:
    Lin, Yunmin (author) / Chen, Caixing (author) / Dai, Jinteng (author) / Huang, Yong (author)


    Publication date :

    2025-06-27


    Size :

    432578 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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