Meng Wang

I'm working at Waymo on the Perception Data Primary team, started in January 2025.

I received my PhD in the Industrial Engineering and Operations Research (IEOR) at the University of Massachusetts Amherst. My advisor is Dr. Shannon Roberts and I worked as a graduate research assistant at the Roberts Research Group and the Human Performance Lab.

From Jan-May 2024, I was a Research Intern at Honda Research Institute in the Human-Centric Intelligence group. I worked on investigating drivers' prosocial behavior. This also involved the application of ML/CV and LLMs to analyze and interpret the multi-modal 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.

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Research & Publications

My research focuses are Large Language Models (LLMs), Machine Learning, and human-computer interaction in the driving domain. Representative papers are highlighted.

Introducing a two-stage framework that integrates roadway complexity features for crash prediction. The model achieves an accuracy of 90.15% with the added complexity-infused features.

Proposing a unified model for Cognitive Load and Eye Region Analysis. It achieves precise keypoint detection and spatiotemporal tracking in a joint-learning framework.

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.

A simulator study implementing staged and simultaneous HMIs and analyzing the drivers' driving and glance behavior.

A literature review of the HMI recommendations and vehicle automations.

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.

A simulator study assessing the effectiveness of training and warning systems on drivers' response behavior to vehicle cyberattacks. .

Understanding the relationship between the drivers' survey data and the responses to vehicle cyberattacks.

Investigating the drivers' cautionary behavior when facing vehicle cyberattacks.

Assessing the impact of level 2 automation and ADHD symptomatology on young drivers' attention maintenance.

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.