Abstract
Enhancing vehicle emergency braking performance is crucial for vehicular safety and reliability. We have observed that the traditional vehicle dynamics based model predictive control (MPC) algorithm which is used to produce emergency braking strategy fails to achieve the minimization of emergency braking distance. To address this issue, this article employed a simulation based approach to improve emergency braking performance via machine learning based algorithms. We design a data optimization model which optimizes the longitudinal forces and slip ratios of the four wheels based on back-propagation neural network (BPNN) and constraint optimization algorithm to reduce emergency braking distance. In addition, a Balltree nearest neighbor search based generator is proposed to produce superior emergency braking strategies for real-time purpose. The experimental results demonstrate that the emergency braking strategy which we optimized achieves an average enhancement of 13% in emergency braking performance compared to the traditional MPC algorithm. Unlike other machine learning based algorithms that have a delay of over 0.01 s, the proposed generator has an execution time of 0.0008 s, meeting the delay requirements under emergency braking conditions.
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Data availability
The data that support the findings of this study are available from the corresponding author Zhang upon reasonable request.
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Funding
This work was partially supported by the National Natural Science Foundation of China (62171285), Natural Science Foundation of Shanghai Municipality (20ZR1440500) and General Research Fund of Shanghai Normal University (KF2021100 and Sk201220).
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Rui Xu, Shijie Xu contributed to the conception and design of this study. Rui Xu, Xiangfen Zhang and Feiniu Yuan performed the experiment and evaluated the model performance. Peng Jiang, Rui Xu collected and processed the datasets. Xiqiang Guan, Tianyi Lu, Ruifeng Zhai, Yixuan Wang contributed to the theoretical research. Xiangfen Zhang, Rui Xu, Ziyou Che and Wenhao Lu contributed significantly to result analysis and manuscript preparation. Figures 1, 2 and 3 were drawn by Rui Xu and Xiangfen Zhang. Fig. 4 was drawn by Shijie Xu. Rui Xu, Xiangfen Zhang and Feiniu Yuan contributed to the manuscript revision.
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Xu, R., Xu, S., Jiang, P. et al. Constraint optimization and key factor analysis based vehicle emergency braking strategy generator. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41679-w
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DOI: https://doi.org/10.1038/s41598-026-41679-w
