Meng Wang

Meng Wang | 王濛

I'm a 3rd year PhD candidate in the Industrial Engineering and Operations Research program (IEOR) at the University of Massachusetts Amherst. My advisor is Dr. Shannon Roberts and I'm a graduate research assistant at the Roberts Research Group and the Human Performance Lab.

From Jan-May 2024, I will be a Research Intern at Honda Research Institute in the Human-Centric Intelligence group. I will be working on investigating drivers' wellbeing levels and forecasting driver behavior using multi-modal data. This will also involve the application of ML/CV and LLMs to analyze and interpret the collected data.

Before UMass, I was a Machine Learning Engineer at MIT AgeLab. I worked on leveraging machine learning and computer vision techniques to analyze drivers' cognitive load and glance patterns, using cloud computing platforms such as Amazon Web Services.

Email / CV / Scholar / LinkedIn

Research

My research focuses are road safety, automated vehicles, driving behavior modeling, human factors, and computer vision applications on roadway scenes. Representative papers are highlighted.

	Human-Machine Interfaces and Vehicle Automation: The Effect of HMI Design on Driver Performance and Behavior Meng Wang, Jah'inaya Parker, Nicholas Wong, Shashank Mehrotra, Shannon C Roberts, Woon Kim, Alicia Romo, William J Horrey Technical Report at AAAFTS, 2023 A simulator study implementing the identified HMI recommendations and analysing the driving behavior.
	Human-Machine Interfaces and Vehicle Automation: A Review of the Literature and Recommendations for System Design, Feedback, and Alerts Shashank Mehrotra, Meng Wang, Nicholas Wong, Jah'inaya Parker, Shannon C Roberts, Woon Kim, Alicia Romo, William J Horrey Technical Report at AAAFTS, 2022 A literature review of the HMI recommendations and vehicle automations.
	Risk-ATTEND (Risk Anticipation Training to Enhance Novice Driving): Pilot Evaluation of a Risk Anticipation Training Program for Teen Drivers Jaji Pamarthi, Apoorva Hungund, Meng Wang, Tina Sayer, Jason Hallman, Shannon Roberts, Anuj K Pradhan Human Factors and Ergonomics Society, 2023 (Oral Presentation) Evaluating an updated training program (based on RAPT and ACCEL) by introducing a more diverse set of training scenarios and leveraging deployment platforms that were better suited to modern devices.
	The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks Fangda Zhang, Meng Wang, Jah'inaya Parker, Shannon C Roberts Safety, 2023 Understanding the relationship between the drivers' survey data and the responses to vehicle cyberattacks.
	How do drivers respond to vehicle cyberattacks? A driving simulator study Jah'inaya Parker, Fangda Zhang, Meng Wang, Shannon C Roberts Presented at Human Factors and Ergonomics Society, 2022, Poster Investigating the drivers' cautionary behavior when facing vehicle cyberattacks.
	Impact of level 2 automation and ADHD symptomatology on young drivers' attention maintenance Meng Wang, Jah'inaya Parker, Beatrice Ojuri, Shannon C Roberts, Jennifer McDermott, Donald L Fisher Transportation Research Part F: Traffic Psychology and Behaviour, 2023 Assessing the impact of level 2 automation and ADHD symptomatology on young drivers' attention maintenance.
	CLERA: A Unified Model for Joint Cognitive Load and Eye Region Analysis in the Wild Li Ding, Jack Terwilliger, Aishni Parab, Meng Wang, Lex Fridman, Bruce Mehler, Bryan Reimer ACM Transactions on Computer-Human Interaction, 2023 Proposing a unified model for Cognitive Load and Eye Region Analysis. It achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework.
	Traffic Safety Impact of the COVID-19 Pandemic: Fatal Crashes Relative to Pre-Pandemic Trends, United States, May-December 2020 Brian C. Tefft, Meng Wang Work conducted during an internship at AAA Foundation for Traffic Safety, 2022 summer Research Brief at AAAFTS / Presented at Transportation Research Board, 2022, Poster Advancing the understanding of how safety on U.S. roads changed during the pandemic the number of fatal crashes during May through December 2020 to what would have been expected had the pandemic not occurred and pre-pandemic trends continued.
	MIT-AVT Clustered Driving Scene Dataset: Evaluating Perception Systems in Real-World Naturalistic Driving Scenarios Li Ding, Michael Glazer, Meng Wang, Bruce Mehler, Bryan Reimer, Lex Fridman IEEE Symposium on Intelligent Vehicle, 2020 Proposing an automated method for clustering common cases and detecting edge cases based on the visual characteristics of the external scene using deep learning and developing a large-scale real-world video driving scene dataset of edge cases and common cases.