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Emergency Steering Modelling of Vehicles via Ensemble Radial Basis Function Neural Network(RBFNN)

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Abstract

Road traffic injuries from rear-end collisions are one of the leading causes of death from vehicular collisions. An accurate driver model must be developed for autonomous vehicles to prevent such accidents. Existing models such as the conventional car-following models have been created for obstacle avoidance adopted by the parametric modeling techniques. Mentioned technique falls short due to the modeling relationship between the vehicle and the human’s driver reaction. Therefore, this study aims to develop an ensemble radial basis function neural network (RBFNN) to produce the predicted steering angle for the driver model in emergency collision avoidance. The data is collected from the ground truth rear-end collision experiment where professional drivers are involved. The variables used during modeling are relative velocity, relative distance, yaw rate, and steering angle. The performance of the developed models is determined by accessing the value of the root mean square error (RMSE) and coefficient of determination, \(R^{2}\) from the generalisation test. In addition, the comparison analysis between the proposed model and standalone RBFNN as well as feed-forward artificial neural network (FFANN) are conducted. Based on the results, ensemble RBFNN produced an outstanding performance in predicting the steering angle.

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Abbreviations

RBFNN:

Radial Basis Function Neural Network

FFANN:

Feed Forward Artificial Neural Network

RMSE:

Root Mean Square Error

ADAS:

Advance Driving Assistance System

CA:

Collision Avoidance

CPN:

Counter Propagation Network

BPN:

Back Propagation Network

TTC:

Time to Collision

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Acknowledgements

Highly appreciation to the research team members from UCSI University, Universiti Teknologi MARA, Cawangan Terengganu, Kampus Dungun, Universiti Malaysia Pahang, and Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil for the dedication and contribution throughout this study.

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Authors and Affiliations

  1. Faculty of Engineering and Built Environment, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia

    Ng Tsu Yao

  2. School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 23000, Dungun, Malaysia

    Nurhaffizah Hassan

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600, Pekan, Pahang, Malaysia

    Sarah ’Atifah Saruchi

  4. Centre of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 43800, Dengkil, Selangor, Malaysia

    Nor Aziyatul Izni

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  1. Ng Tsu Yao
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  2. Nurhaffizah Hassan
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  3. Sarah ’Atifah Saruchi
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  4. Nor Aziyatul Izni
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Correspondence to Nurhaffizah Hassan.

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Yao, N.T., Hassan, N., Saruchi, S.’. et al. Emergency Steering Modelling of Vehicles via Ensemble Radial Basis Function Neural Network(RBFNN). Int. J. ITS Res. 21, 319–330 (2023). https://doi.org/10.1007/s13177-023-00356-2

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