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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

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

Rui Xu¹, Shijie Xu¹, Peng Jiang¹

¹College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, China.

Scientific Reports

|February 27, 2026

Summary

This summary is machine-generated.

This study improves vehicle emergency braking distance using machine learning. A novel strategy enhances braking performance by 13% compared to traditional methods, ensuring faster response times for safety.

Keywords:

constraint optimization emergency braking key factor analysis model predictive control nearest neighbor search

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Area of Science:

Automotive Engineering
Machine Learning
Control Systems

Background:

Traditional model predictive control (MPC) for emergency braking struggles to minimize braking distance.
Vehicle safety and reliability depend on effective emergency braking performance.

Purpose of the Study:

To enhance vehicle emergency braking performance by reducing stopping distance.
To develop a machine learning-based approach superior to traditional MPC.

Main Methods:

A simulation-based approach utilizing machine learning algorithms.
A data optimization model with back-propagation neural networks (BPNN) and constraint optimization.
A Balltree nearest neighbor search-based generator for real-time strategy generation.

Main Results:

The optimized emergency braking strategy achieved an average 13% improvement in braking performance.
The proposed generator demonstrated a rapid execution time of 0.0008 s, meeting real-time requirements.
The new strategy significantly reduces emergency braking distance compared to traditional MPC.

Conclusions:

Machine learning, specifically the proposed BPNN and Balltree methods, offers a superior approach to emergency braking.
The developed strategy enhances vehicle safety by minimizing emergency braking distance effectively.
The real-time capabilities of the generator are suitable for critical braking scenarios.