Evidence map›Paper›PMID 41760712›Full record

ArticleScientific reports2026

Constraint optimization and key factor analysis based vehicle emergency braking strategy generator.

Rui Xu, Shijie Xu, Peng Jiang, Xiangfen Zhang, Feiniu Yuan, Xiqiang Guan, Ziyou Chen, Ruifeng Zhai, Yixuan Wang, Tianyi Lu and 1 more

Abstract read
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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Rui XuCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Shijie XuCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Peng JiangCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Xiangfen ZhangCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China. xiangfen@shnu.edu.cn.
Feiniu YuanCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Xiqiang GuanCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China. xqguan@shnu.edu.cn.
Ziyou ChenKing's University College, University of Western Ontario, London, Canada.
Ruifeng ZhaiCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Yixuan WangCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Tianyi LuCollege of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.
Wenhao LuDepartment of Electrical and Computer Engineering, University of Alberta, Alberta, Canada.

Funding

General Research Fund of Shanghai Normal University KF2021100National Natural Science Foundation of China 62171285Natural Science Foundation of Shanghai Municipality 20ZR1440500
6 · The paper itself

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.

Indexed as

constraint optimizationemergency brakingkey factor analysismodel predictive controlnearest neighbor search

Identifiers

PMID41760712
PMCPMC13049023

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.