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TECHNICAL PAPERS

Rollover Warning for Articulated Heavy Vehicles Based on a Time-to-Rollover Metric

[+] Author and Article Information
Bo-Chiuan Chen

Department of Vehicle Engineering,  National Taipei University of Technology, Taipei 106, Taiwanbochen@ntut.edu.tw

Huei Peng

Department of Mechanical Engineering,  University of Michigan, Ann Arbor, MI 48109-2133hpeng@umich.edu

J. Dyn. Sys., Meas., Control 127(3), 406-414 (Oct 19, 2004) (9 pages) doi:10.1115/1.1988340 History: Received July 15, 2003; Revised October 19, 2004

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately “counts-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately assessed. There are two conflicting requirements in the implementation of TTR. On the one hand, a model significantly faster than real-time is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the design process is illustrated in an example. First, a simple yet reasonably accurate yaw∕roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simple model. The NN takes the TTR generated by the simple model, vehicle roll angle, and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achieved across all the driving scenarios we tested.

Copyright © 2005 by American Society of Mechanical Engineers
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Figures

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Figure 1

Definition of TTR

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Figure 2

Flow chart for the TTR calculation

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Figure 3

Structure of the neural network

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Figure 4

Severity of the NN training maneuvers

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Figure 6

Structure of the simple decoupled yaw-roll model

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Figure 7

Configuration of the four simple roll models

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Figure 8

Bicycle-towing-unicycle yaw model of an articulated heavy truck

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Figure 9

ARCSIM and simple model response under an obstacle avoidance maneuver

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Figure 10

Four types of maneuvers

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Figure 11

Block diagram of the driver model

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Figure 12

Simple TTRs and ARCSIM TTR

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Figure 13

ARCSIM responses under the obstacle avoidance maneuver

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Figure 14

NN-TTR results without worst-case training scenario

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Figure 15

NN-TTR results with and without worst-case training scenario

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Figure 16

Free body diagram of the tractor unsprung mass

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Figure 17

Free body diagram of the tractor sprung mass

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