A Data- and Model-Integrated Driven Method for Recommending the Maximum Safe Braking Deceleration Rates for Trucks on Horizontal Curves

Truck skidding crashes on horizontal curves pose a significant road safety concern, with improper braking being the primary cause. A data- and model-integrated driven method is proposed to investigate the mechanism and recommend the maximum safe braking deceleration rates without skidding (abbreviated as MSBDRs) for trucks on horizontal curves. Firstly, a comprehensive road–vehicle interaction model was developed, considering dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, leveraging the “HighD” data set and employing cluster analysis principles, parameter data were extracted using Python and Matlab. Finally, through parameterizing model inputs, the transient dynamic response of trucks was examined, the potential of truck skidding was predicted, and the MSBDRs were recommended. The results indicate the following. (1) There is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. (2) The MSBDR is 4.5 m/s2 on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s2, 3 m/s2, and 2 m/s2. These results provide a theoretical foundation for braking strategies in autonomous vehicles.

Authors

Institutions

Publication Details

Journal
Applied Sciences
Published
2024-10-14
DOI
https://doi.org/10.3390/app14209357
Citations
1
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.20
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Data- and Model-Integrated Driven Method for Recommending the Maximum Safe Braking Deceleration Rates for Trucks on Horizontal Curves

Tian Xin, Jinliang Xu
1 citations
Applied Sciences
Autonomous Vehicle Technology and Safety
0.20
article

A Data- and Model-Integrated Driven Method for Recommending the Maximum Safe Braking Deceleration Rates for Trucks on Horizontal Curves

Tian Xin, Jinliang Xu
article en
1 citations

Abstract

Truck skidding crashes on horizontal curves pose a significant road safety concern, with improper braking being the primary cause. A data- and model-integrated driven method is proposed to investigate the mechanism and recommend the maximum safe braking deceleration rates without skidding (abbreviated as MSBDRs) for trucks on horizontal curves. Firstly, a comprehensive road–vehicle interaction model was developed, considering dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, leveraging the “HighD” data set and employing cluster analysis principles, parameter data were extracted using Python and Matlab. Finally, through parameterizing model inputs, the transient dynamic response of trucks was examined, the potential of truck skidding was predicted, and the MSBDRs were recommended. The results indicate the following. (1) There is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. (2) The MSBDR is 4.5 m/s2 on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s2, 3 m/s2, and 2 m/s2. These results provide a theoretical foundation for braking strategies in autonomous vehicles.

Applied SciencesVol. 14(20)
Chang'an University (CN)
Openalex Percentile: Top 49%
Autonomous Vehicle Technology and Safety
0.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

A Data- and Model-Integrated Driven Method for Recommending the Maximum Safe Braking Deceleration Rates for Trucks on Horizontal Curves — Tian Xin, Jinliang Xu · Applied Sciences (2024) | TGRS Research Map | TGRS