Modeling the Movement of Vehicles with an Anti-lock Braking System on Various Types of Road Surface Using the Principles of PID Control

Boyko, Grigory; Fedin, Alexey; Petrenko, M.; Leskovets, I.; Redl, Jozef

doi:10.1007/978-3-031-44615-3_11

Communications in Computer and Information Science

2023

DOI: 10.1007/978-3-031-44615-3_11

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Modeling the Movement of Vehicles with an Anti-lock Braking System on Various Types of Road Surface Using the Principles of PID Control

Grigory Boyko,

Alexey Fedin,

M. Petrenko

et al.

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2024

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Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

Gunjate,

Khot

2024

Eng. Res. Express

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Previous research on predicting automotive response in antilock braking systems faced several common drawbacks. Limited availability of high-quality datasets and computational constraints hindered the models' ability to generalize and perform effectively in real-time. Additionally, integrating these models into existing vehicle systems is challenging, often requiring extensive calibration and testing to ensure reliability and safety. This study focuses on enhancing automotive safety through the integration of machine learning with advanced braking systems. An antilock braking system model is developed using MATLAB, accounting for key parameters such as speed, mass, wheel radius, and moment of inertia. Various probability ranges are simulated to evaluate the system's real-time response in dynamic environments. A supervised machine learning approach is applied, with lasso regression used for model validation. Braking response was evaluated at three speeds: 25 km/h, 45 km/h, and 65 km/h. To assess model accuracy, error analysis was conducted using metrics such as mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE). Results showed a significant improvement in prediction accuracy, with MAPE reduced to 3.2%, MAE to 0.15 seconds, RMSE to 0.25 seconds, and MSE to 0.06 seconds.A comparative analysis is done using the Python Jupyter Framework, further validating the findings. The machine learning model successfully forecasts braking response times, and validates its prediction against random datasets.

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Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

Gunjate,

Khot

2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

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Modeling the Movement of Vehicles with an Anti-lock Braking System on Various Types of Road Surface Using the Principles of PID Control

Cited by 1 publication

References 9 publications

Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

Prediction of automotive response using supervised machine learning in antilock braking systems and comparison with different models for improved vehicle safety

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