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.

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Fig. 1

Standard processes in man–machine systems

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Fig. 2

Concept of Type I human uncertainty

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Fig. 3

Concept of Type II human uncertainty

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Fig. 4

Concept of Type III human uncertainty

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Fig. 5

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

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Fig. 6

Method of compensation for human uncertainty

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Fig. 7

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

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Fig. 8

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

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Fig. 9

Flowchart of the intelligent parking assist system

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Fig. 10

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

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Fig. 11

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

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Fig. 12

Vehicle and path models in VI sequence generation

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Fig. 13

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

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Fig. 14

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

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Fig. 15

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

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Fig. 16

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

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Fig. 17

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

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Fig. 18

Comparison of results considering operation uncertainty

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Fig. 19

Experimental configuration

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Fig. 20

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




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