The Expert Locomotive Engineer’s Mental Model

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The Expert Locomotive Engineer’s Mental Model

2021-08-01
By Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics ; General Electric Company

File Language:

English

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Corporate Creators:

Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics ; General Electric Company
Corporate Contributors:

United States. Department of Transportation. Federal Railroad Administration. Office of Railroad Policy and Development
Subject/TRT Terms:
Publication/ Report Number:

RR 21-15
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

United States. Department of Transportation. Federal Railroad Administration
Abstract:

Researchers from General Electric (GE) Research and the Massachusetts Institute of Technology (MIT) Human Systems Lab are studying ways to improve the man-machine interface for the locomotive driving task and train handling with the assistance of advanced automation. This man-machine collaborative approach to the design of automated control systems promises improved locomotive control for both experienced engineers and those new to the job. The system design approach is to build-in and take advantage of expert drivers’ knowledge and skills and the machine’s ability to execute instructions more precisely than the engineer to provide better overall safety and efficiency. From October 2019 to July 2020, the team conducted the experiments at the Federal Railroad Administration’s (FRA) Cab Technology Integration Lab (CTIL) (Figure 1). These experiments put expert and novice drivers in the simulated cab, allowing each to drive while blocking the view out the window from the driver (i.e., forcing interaction with the non-driving participant) and recording their interactions for analysis.
Format:

PDF
Funding:

693JJ6-18-C-000031
Download URL:

https://rosap.ntl.bts.gov/view/dot/57310/dot_57310_DS1.pdf
File Type:

[PDF - 501.12 KB]
Collection(s):

Federal Railroad Administration
Main Document Checksum:

urn:sha256:8f0d285eeafdecfbc2802bcd780d6e768e4c9bebea19a020e717ad785ee4ba56

File Language:

English

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