Xingjian Ma

Human Factors Engineering PhD Student

About Me

Xingjian Ma Profile Photo
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Email
xma342@wisc.edu
Location
Madison, WI

I am a third-year Ph.D. student in Industrial and Systems Engineering at the University of Wisconsin-Madison, specializing in human behavior modeling within human-machine systems. My research focuses on understanding how humans adapt to and behave within automated technologies, particularly studying remote operation of highly automated vehicles and exploring the development of trust and reliance in human-automation interactions.

I am currently seeking Spring/Summer 2026 internship opportunities as a Human Factors Researcher/Engineer or User Experience Researcher. I am eager to apply my expertise in human factors research and machine learning to real-world challenges, with particular interest in opportunities related to driving/transportation safety and human-machine interaction.

Education:

Doctor of Philosophy
Industrial and Systems Engineering
University of Wisconsin-Madison • Expected 2027

Master of Science
Electrical and Computer Engineering
University of Florida • 2023

Bachelor of Engineering
Electronic and Information Engineering
Xidian University • 2020

Research Interests:

Human-Automation Interaction, Human-Machine Systems, Human Behavioral Modeling, Driving Automation, Remote Driving, Trust in Automation

Research Projects

Pathways to Remote Operation for Automated Trucking ONGOING

Automation will play a crucial role in the future of trucking; however, remote operators will remain essential for maintaining efficiency and safety. This project explores optimization methods for remote driving systems, focusing on latency management, multi-vehicle control modeling, and novel control algorithm integration.

Sponsored by: National Science Foundation (NSF)

Dynamic Trust and Reliance in Conditional Driving Automation ONGOING

Although automated vehicles promise enhanced safety and mobility, public concerns during human-automation transitions reveal the need for calibrated trust. This project develops neural-based trust measures and behavioral models to ensure driver trust aligns with system capabilities through comprehensive simulation studies.

Sponsored by: National Science Foundation (NSF)

Naturalistic Driving Studies and Healthcare ONGOING

Driving represents a critical activity of daily living and serves as a health benchmark for individuals with acute and chronic conditions. This research series explores naturalistic driving patterns and employs qualitative methods to understand driving transitions and develop evidence-based tools for enhancing driving safety.

Understanding Visual Scanning Behavior in Driving

Eye-tracking data provides crucial insights into driver behavior, yet current methods rely on oversimplified representations or excessive computational resources. This research presents a novel statistical approach that efficiently extracts unique visual scanning patterns from driving data using advanced time series analysis.

In-Vehicle Automation for Parkinson's Disease Drivers

This research examines how in-vehicle automation technologies can reduce driving errors and enhance safety for individuals with Parkinson's disease, demonstrating that adaptive cruise control and driver assistance systems effectively compensate for cognitive deficits during real-world on-road testing.

Publications

Peer-Reviewed Journal Papers

What leads to reliance on automated vehicles? An inferential analysis of responses to variable AV performance
Xizi Xiao, Xingjian Ma, Anthony D. McDonald, Ranjana K. Mehta
Published • Applied Ergonomics • 2025
DOI
Identifying Critical Breakpoints and Latency Effects During Remote Backup Driving
Xingjian Ma, Anthony D. McDonald
Under Review • 2025

Conference Presentations & Posters

Understanding Reliance Decisions in Automated Vehicles Using Random Forest Analysis
Xingjian Ma, Xizi Xiao, Ranjana Mehta, Anthony D. McDonald
Human Factors and Ergonomics Society Annual Meeting • Oral Presentation • October 2024
Abstract
Understanding the Workload of Remote Truck Operators with Discrete Event Simulation
Xingjian Ma, Vanik Zakarian, Anthony D. McDonald
Human Factors and Ergonomics Society Annual Meeting • Oral Presentation • October 2024
Abstract
Understanding Visual Scanning Behavior in Driving: A Review and a New Perspective Using Statistical Pattern-Based Approach
Xingjian Ma, Minhee Kim, Haolan Zheng, Wayne C.W. Giang
Transportation Research Board Annual Meeting • Poster Session • January 2024
Poster