Comparative Analysis of Methods to Estimate the Tire/Road Friction Coefficient Applied to Traffic Accident Reconstruction

2020-01-5058

06/04/2020

Features
Event
Automotive Technical Papers
Authors Abstract
Content
This study presents a comparison of three methods: VC4000PC accelerometer, Sensor Kinetics mobile app, and video analysis by Tracker (free software). The methods have been compared experimentally for emergency braking with locked wheels, as well as using the antilock braking system (ABS). Data from the three methods were recorded simultaneously. Data analysis was made, applying inferential statistics. The results from the analysis of variance (ANOVA) and precision analysis indicate that data collected with the smartphone app ensures good accuracy and does not provide significant differences in comparison with the accelerometer, while the video analysis method showed some statistical differences. The regression analysis yielded an estimation coefficient R2 close to 1, and a residual standard error (RSE) being almost 0, for all experiments, parameters that indicated a good linear regression for both low-cost methods as a function of the accelerometer. The confidence interval, absolute error, and other estimators have been obtained for the smartphone method, using the accelerometer as a reference.
Results showed that a smartphone may be an optimal low-cost method, which could be used by the experts to measure parameters on-site following the recommended practices for braking tests with vehicles and applying the measured parameters to the traffic accident reconstruction.
Meta TagsDetails
DOI
https://doi.org/10.4271/2020-01-5058
Pages
11
Citation
Enciso, G., Toresan, W., Baena, A., Londoño, H. et al., "Comparative Analysis of Methods to Estimate the Tire/Road Friction Coefficient Applied to Traffic Accident Reconstruction," SAE Technical Paper 2020-01-5058, 2020, https://doi.org/10.4271/2020-01-5058.
Additional Details
Publisher
Published
Jun 4, 2020
Product Code
2020-01-5058
Content Type
Technical Paper
Language
English