RR 24-01 | February 2024

AN AUTOMATION AWARENESS ASSISTANT FOR 

AUTOMATED TRAIN OPERATIONS

SUMMARY

From August 8, 2022, to January 31, 2024, the 

Federal Railroad Administration (FRA) 

sponsored Monterey Technologies, Inc. to 

design and develop an improved interface 

between human operators and advanced 

automation in rail systems. Applying usercentered design (UCD) methodology, the team 

prototyped and demonstrated a locomotive cab 

user-interface (UI) concept called the 

Automation Awareness Assistant (A3). A3 is 

designed to improve human operator situational

awareness of automated rail functionality and 

provide decision support if a possible 

automation failure is detected. 

A3 supports the human operator cognitive 

monitoring requirements to maintain awareness 

of train automation, enabling recognition of 

anomalies in both system performance and 

operational situation. A3 supports achieving and 

maintaining automation awareness, in turn 

supporting the broad capabilities required to 

meet railroad enterprise safety and performance 

goals. A3 is designed for locomotive cabs, but 

the A3 user interface concept can be applied 

and extended to other rail, transportation, and 

defense systems with human-automation 

interaction.

The Association of American Railroads’ 

Automated Train Operations (ATO) Technical 

Advisory Group (TAG) is developing an 

“automated rail taxonomy,” including a concept 

of operations (ConOps) for interoperable 

aspects of ATO systems for freight rail 

operations (Hunter & Bryant, 2023). To ensure 

rail systems safety, this guidance asserts that 

train automation requires the locomotive crew to 

cross monitor interactions among automation 

systems and subsystems. The A3 human 

machine interface (HMI) concept and UI 

prototype form an interoperability solution with 

assured human control. This solution can

generate automation and UI requirements to 

support the ATO TAG ConOps for adding 

automated functionality into existing and future 

rail systems. The A3 concept can be scaled to 

achieve assured human control for all 

automated functions in any vehicle. 

BACKGROUND

A3 seeks to reduce human error related to rail 

systems design, thereby reducing the likelihood 

of accidents. Human error causes accidents in 

all transportation modes. While automation is 

often intended to reduce the likelihood and limit 

the consequences of human error, inappropriate 

integration of automation into human job tasks 

can have the opposite effect. Accidents and 

incidents in transportation often arise because of 

human operator confusion about precisely what 

complex automated systems are doing. 

Compressed cycle times increase automation 

uncertainty and risk by introducing higher human 

workloads into the rail operational environment. 

A major goal of the A3 technical solution is to 

mitigate risks associated with human-on-theloop (HOTL) rail system control, for which

machine errors and brittle automation could lead 

to cascading failures. HOTL here refers to 

management of the automation to drive a train. 

As rail system automation advances, human 

interaction requirements should be specified, 

defining assured human control authority: how 

the human operator will reassert control and 

retain decision-making while minimizing critical 
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RESEARCH RESULTS 2 | Page

function activation delays. For HOTL rail 

systems, developing a locomotive cab display to 

optimize human-machine function allocation 

could help assure human control. This would

provide sufficient insight into and human control 

of the system to be able to intervene if the 

automation malfunctions. 

UCD, a human task-centered approach, 

organizes system information and controls in a 

human activity-centric manner such that normal 

workflows are efficient and task products can be 

easily created. Information is “brought to the 

task” instead of requiring the end user to collect, 

gather, and synthesize information from 

separate sources. Using UCD to display 

information and assure human control of 

automated rail systems will involve the trade-off 

of function allocations between human and 

systems for completing task steps and 

accomplishing goals. UCD methodology 

includes UI constructs that support function 

allocation decisions to deliver capability as 

safety cycle time approaches zero. This can 

include shared system-user task states and 

awareness of past, current, and planned tasks. 

OBJECTIVES

The objective of this project was to apply UCD to

develop a locomotive engineer HMI that displays 

correct and timely information and functionality 

to manage, adapt to, or override potentially

unsafe automated rail equipment behaviors. The 

HMI design solution will provide necessary 

situational awareness of rail systems’ states, 

processes, and operational environment so the 

locomotive human crew can adapt to 

unexpected situations. The HMI concept will 

support the monitoring and control of currentgeneration and future automated locomotive and 

rail infrastructure capabilities, features, and 

functions.

METHODS

UCD is an agile, human task-centered process, 

conducted as an iterative design-test spiral, 

using interim results to devise incremental, 

measurable improvements in both hardware and 

software. The goal is to identify and address 

user needs that are not well-supported in the 

system, leading to a more intuitive UI. As each 

design cycle increases the prototype fidelity, 

usability analysis provides design feedback for 

inclusion in the next product evolution. UCD 

involves iterative drill-down into job task and 

sub-task details until the project team 

determines that sufficient detail is gathered to 

enable software development. This approach 

follows a task-centered design technique in 

which emerging visualizations of rail systems 

are presented and evaluated at each design 

step, to be discussed by end-users and 

designers. This provides a team approach to the 

visualization of HMI concepts, as opposed to 

traditional design processes with textual 

descriptions of requirements. UCD focuses on 

achieving enterprise capabilities through

continuous discovery and the flexibility to 

experiment with creative ideas that may lead to 

insight and innovation. As part of the design 

cycle, these ideas are molded to fit developer 

resources and software architecture, configuring

the ideas into realistic prototypes. 

RESULTS

A3 UI constructs were derived from an analysis 

of locomotive systems, job tasks, and workflows 

and assembled into a notional UI wireframe 

(Figure 1) and an initial mockup. Employing 

UCD methods, the mockup was simplified to a 

low-fidelity prototype, then to a high-fidelity 

prototype (Figure 2). The team developed 

preliminary software to realize the UI prototype 

in the locomotive cab and successfully tested it

at FRA’s Cab Technology Integration Laboratory 

(CTIL) located at the U.S. Department of 

Transportation, Volpe National Transportation 

Systems Center, Cambridge, MA. 
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RESEARCH RESULTS 3 | Page

Figure 1. Notional wireframe of A3 UI constructs

Figure 2. A3 UI high-fidelity prototype

CONCLUSIONS

To make sense of large amounts of rail systems 

data, the locomotive crew needs HMI assistance 

to manage surges in workload from unexpected 

automation behaviors. The A3 decision support 

application will provide the locomotive engineer

with the necessary awareness of the operational 

environment and rail systems’ status to override 

or adapt train automation in unexpected 

situations. To improve situational awareness 

and operations safety, the team built and 

evaluated an A3 UI prototype, containing UI 

constructs for critical information and functions 

to manage train automation. A3 will support 

gathering information or affordance to progress 

in detecting and classifying faults occurring 

under automated control. A3 is a total systems 

HMI solution: a cab console display that will 

provide the awareness necessary for the human 

operator to override or adapt to rail systems in 

unexpected situations.

FUTURE ACTION

Railroads are accelerating investment in 

automation technology, telematics, artificial 

intelligence, and other advanced capabilities for

real-time and near real-time operations. An 

automation awareness application was 

prototyped and demonstrated for integrating 

these emerging technologies into railroad 

operations and infrastructure. The prototype is 

available for evaluation and further refinement 

with reasonable expectation that it will 

measurably improve railroad bottom line 

performance and achievement of safety goals. 

This demonstration was in the context of a 

freight locomotive cab to help visualize and 

evaluate the A3 prototype, but its architecture 

and demonstrated principles apply at all levels of 

railroad infrastructure.

REFERENCES 

Hunter, D. C., & Bryant, Q. (2023). Automated 

Train Operation Interface Requirements 

Specification Development Summary Report 

(DOT/FRA/ORD-23/31). Washington DC: 

Federal Railroad Administration.
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RESEARCH RESULTS 4 | Page

ACKNOWLEDGEMENTS

These results are part of a larger work 

completed by Monterey Technologies, Inc. and 

Rational Blue, LLC. 

CONTACT

Michael E. Jones

Engineering Psychologist

Federal Railroad Administration 

Office of Research, Data, and Innovation 

1200 New Jersey Avenue, SE

Washington, DC 20590

(202) 493-6106

Michael.E.Jones@dot.gov

Michael B. Cowen

Human Factors Engineer

Monterey Technologies, Inc. 

1398 Center Drive, Suite 310

Park City, UT 84098

(619) 813-8125

mcowen@mti-inc.com

Steven D. Harris 

Human Factors Engineer

Rational Blue, LLC

107 Areneuse Creek Court 

Herford, NC 27944 

(252) 339-3423

sharris@rationalblue.com

KEYWORDS

Decision support, human-automation interaction,

human factors, rail automation, situation 

awareness, train control, train handling

CONTRACT NUMBER

693JJ622C000016