0
Research Papers

Compensating for Operational Uncertainty in Man–Machine Systems: A Case Study on Intelligent Vehicle Parking Assist System

[+] Author and Article Information
Dale Su

Department of Mechanical Engineering, National Cheng Kung University, Tainan 70101, Taiwan

Kuei-Yuan Chan

Department of Mechanical Engineering, National Taiwan University, Taipei 10617, Taiwan e-mail: chanky@ntu.edu.tw

1Corresponding author.

Manuscript received June 12, 2014; final manuscript received January 28, 2015; published online July 1, 2015. Assoc. Editor: Alba Sofi.

ASME J. Risk Uncertainty Part B 1(3), 031008 (Jul 01, 2015) (13 pages) Paper No: RISK-14-1027; doi: 10.1115/1.4030438 History: Received June 12, 2014; Accepted April 27, 2015; Online July 01, 2015

The successful operation of man–machine systems requires consistent human operation and reliable machine performance. Machine reliability has received numerous improvements, whereas human-related operational uncertainty is an area of increasing research interest. Most studies and formal documentation only provide suggestions for alleviating human uncertainty instead of providing specific methods to ensure operation accuracy in real-time. This paper presents a general framework for a reliable system that compensates for human-operating uncertainty during operation. This system learns the response of the user, constructs the user’s behavior pattern, and then creates compensated instructions to ensure the completion of the desired tasks, thus improving the reliability of the man–machine system. The proposed framework is applied to the development of an intelligent vehicle parking assist system. Existing parking assist systems do not account for driver error, nor do they consider realistic urban parking spaces with obstacles. The proposed system computes a theoretical path once a parking space is identified. Audio commands are then sent to the driver with real-time compensation to minimize deviations from the path. When an operation is too far away from the desired path to be compensated, a new set of instructions is computed based on the collected uncertainty. Tests with various real-world urban parking scenarios indicated that there is a possibility to park a vehicle with a space that is as small as 1.07 times the vehicle length with up to 30% uncertainty. Results also show that the compensation scheme allows diverse operators to reliably achieve a desired goal.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Fig. 1

Standard processes in man–machine systems

Grahic Jump Location
Fig. 2

Concept of Type I human uncertainty

Grahic Jump Location
Fig. 3

Concept of Type II human uncertainty

Grahic Jump Location
Fig. 4

Concept of Type III human uncertainty

Grahic Jump Location
Fig. 5

Concept of Type IV human uncertainty: (a) Comparison of different machine and (b) comparison of different instructions

Grahic Jump Location
Fig. 6

Method of compensation for human uncertainty

Grahic Jump Location
Fig. 7

Ford parking assist system: (a) Ford parallel parking assist system patent flowchart [25] and (b) Ford parallel parking assist system diagram [26]

Grahic Jump Location
Fig. 8

ARTC parking assist system: (a) ARTC parallel parking assist system patent flowchart [27] and (b) ARTC parallel parking assist system diagram [28]

Grahic Jump Location
Fig. 9

Flowchart of the intelligent parking assist system

Grahic Jump Location
Fig. 10

Parking path generation from [30]: (a) Path comparison and (b) steering comparison

Grahic Jump Location
Fig. 11

Circle path [31]: (a) Circle path simulation via Matlab and (b) summary of geometric calculations for several commercial vehicles

Grahic Jump Location
Fig. 12

Vehicle and path models in VI sequence generation

Grahic Jump Location
Fig. 13

Simulation results: (a) Sequence of steering angle and (b) parking trajectory

Grahic Jump Location
Fig. 14

Different types of parking scenario: (a) Normal, (b) narrow alley, and (c) obstacles

Grahic Jump Location
Fig. 15

Normal parking scenario results with no driver error: (a) Parking trajectory and (b) VI sequence of steering angle

Grahic Jump Location
Fig. 16

Narrow alley parking scenario results with no driver error: (a) Parking trajectory and (b) VI sequence of steering angle

Grahic Jump Location
Fig. 17

Obstacles parking scenario results with no driver error: (a) Parking trajectory and (b) VI sequence of steering angle

Grahic Jump Location
Fig. 18

Comparison of results considering operation uncertainty

Grahic Jump Location
Fig. 19

Experimental configuration

Grahic Jump Location
Fig. 20

Comparisons of minimal parking space: (a) Comparison of experiment and simulation and (b) parking trajectory simulation

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Articles from Part A: Civil Engineering
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In