A new method for vehicle system safety design based on data mining with uncertainty modeling
Xianping Du, Binhui Jiang, Feng Zhu

TL;DR
This paper introduces a novel data mining-based design approach incorporating uncertainty modeling for vehicle safety, enabling efficient, reliable, and multi-level design of crashworthy cars using decision trees and finite element analysis.
Contribution
It develops a new decision tree algorithm for uncertain data and integrates it with crash simulation data to improve vehicle safety design under uncertainty.
Findings
Enhanced design efficiency and effectiveness.
Increased reliability of final vehicle safety designs.
Potential for broad application to complex mechanical systems.
Abstract
In this research, a new data mining-based design approach has been developed for designing complex mechanical systems such as a crashworthy passenger car with uncertainty modeling. The method allows exploring the big crash simulation dataset to design the vehicle at multi-levels in a top-down manner (main energy absorbing system, components, and geometric features) and derive design rules based on the whole vehicle body safety requirements to make decisions towards the component and sub-component level design. Full vehicle and component simulation datasets are mined to build decision trees, where the interrelationship among parameters can be revealed and the design rules are derived to produce designs with good performance. This method has been extended by accounting for the uncertainty in the design variables. A new decision tree algorithm for uncertain data (DTUD) is developed to…
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Taxonomy
TopicsTransportation Safety and Impact Analysis · Autonomous Vehicle Technology and Safety · Automotive and Human Injury Biomechanics
