UMTRI 2022 SURE Research Projects

UMTRI Project #1: Adaptive Safety Designs for Injury Prevention: Human Modeling and Impact Simulations
Faculty Mentor: Jingwen Hu,


  • Proficiency in Matlab
  • Interested in injury biomechanics research
  • Demonstrated ability in FE model development and application is a plus

Project Description: Unintentional injuries, such as those occurred in motor vehicle crashes, falls, and sports are a major public health problem worldwide. Finite element (FE) human models have the potential to better estimate tissue-level injury responses than any other existing biomechanical tools. However, current FE human models were primarily developed and validated for midsize men, and yet significant morphological and biomechanical variations exist in human anatomy. The goals of this study are to develop parametric human FE models accounting for the geometric variations in the population, and to conduct a feasibility study using population-based simulations to evaluate the influence of human morphological variation on human impact responses in motor-vehicle crashes and sport-related head impacts. Specifically, in this study, students will use medical image analysis and statistical methods to quantify the geometric variance of the skeleton among the population; use mesh morphing methods to rapidly morph a baseline human FE model to a large number of human models with a wide range of size and shape for both males and females; and conduct impact simulations with those models toward adaptive safety designs.
Research Mode: Online or Hybrid

UMTRI Project #2: Driver State Monitoring for Automated Vehicles
Faculty Mentor: Monica L.H. Jones,


  • Some experience with scientific programming languages is required (e.g. Mathematica, MatLab, Python)
  • Familiarity with computer vision programming is desired

Project Description: With increasing automation (SAE Levels 2 and 3), the role of the driver will transition from Driver Driving (DD) to Driver Not Driving (DND). However, there is little quantitative information available on naturalistic non-driving occupant behavior. Driver state monitoring (DSM) systems attempt to predict the driver’s readiness to respond to a takeover request or other emerging need within the situation from information obtained from cameras and other sensors. These systems face several challenges to comprehensively track the continuum of possible driver postures and behaviors. This project will explore the characteristics and behaviors associated with non-nominal postures, driver engagement, monitoring, and state levels (Day vs. Night conditions) that are useful for driver state monitoring (DSM) classification. Continuous measures during in-vehicle test conditions include: subjective assessment, 2D image and 3D depth data, physiological response, and other available DSM outputs. The project seeks to quantify driver response to unscheduled automated-to-manual (non-critical) transitions in an L3 automated driving conditions. The results of this study may identify disallowed states and provide further design guidance for DSMs.
Research Mode: In Lab, Online, or Hybrid

UMTRI Project #3: Augmented Virtual Reality (AVR) based Driving Scenario Simulation and Analysis
Faculty Mentor: Shan Bao,
Prerequisites:  Motivated students who are comfortable working with a big group. Having skills of website development is a great plus!!
Project Description: When evaluating and testing automated vehicle technologies, it is pretty challenging and expensive to test the prototype system using real cars on real roads. Ideally, parameter setting of sensors and vehicle control systems can be tested and evaluated under variety of simulated scenarios at first.  This work is sponsored by a mixed of sponsors with several focuses. The work is designed to simulate 2D and/3D real world driving scenarios in the virtual environment through software (e.g., Carla or Carsim) or Virtual Reality techniques. Student interns get to work with the exciting concepts and interact with our industry sponsors directly and will be able to implement your simulation results through hands on experiences. We are looking for multiple motivated student helpers. Training on certain software (e.g., Carla or Carsim) and hardware (AVR-Heads set) are available.
Research Mode: Online or Hybrid

UMTRI Project #4: Safety and Independence of Passengers in Wheelchairs Using Automated Vehicles
Faculty Mentor: Kathleen D. Klinich,
Prerequisites: Strong technical writing skills, experience with spreadsheet/data analysis, mechanical design/controls experience, and an interest in improving user travel experience and working with people who have disabilities.
Project Description: We are pursuing multiple projects to ensure that people who travel while seated in their wheelchairs can safely and independently do so in automated vehicles where there may not be a driver to assist in securing the wheelchair. We plan on reviewing relevant standards to identify what manufacturers need to consider when developing accessible AVs, performing testing with volunteers to evaluate usability of different configurations, develop a side impact test procedure for wheelchairs, and develop hardware components to allow independent use of restraint systems. 
Research Mode: In lab, hybrid

UMTRI Project #5: Motion Sickness to Inform Automated Vehicle Design
Faculty Mentor: Monica L.H. Jones,


  • Some experience with scientific programming languages is required (e.g. Mathematica, MatLab, Python)
  • Familiarity with computer vision programming is desired

Project Description: Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. However, lack of a definitive etiology of motion sickness challenges the design of automated vehicles (AVs) to address and mitigate motion sickness susceptibility effectively. The quantification of motion sickness severity and identification of objective parameters is fundamental to informing future countermeasures. Data were gathered on-road and on the Mcity test facility.  Continuous measures include: subjective assessment, 2D image and 3D depth data, thermal imaging, physiological response and vehicle data. Modeling effort will elucidate relationships among the factors contributing to motion sickness for the purpose of generating hypotheses and informing future countermeasures for AVs.
Research Mode: In Lab, Online, Hybrid

UMTRI Project #6: The Development for Automated Overtaking Features
Faculty Mentor: Brian T. W. Lin,


  • Will help conduct evaluation experiments, collect subjective assessment data in a driving simulator, conduct data analytic methods, and analyze the data with statistical methods
  • Must have taken statistics, human factors, usability assessment, or other related classes
  • Have great communication skills

Project Description: Many bicyclists share the roadway with motor vehicles that drive much faster. Once an accident occurs with bicyclists involved, the death rate of the bicyclist is extremely high. To date there is no mature and reliable technology that helps drivers overtake bicyclists safely and can be as well as accepted by bicyclists. This study follows a systematic method to develop a prototype for an automated overtaking system, specifically for overtaking bicyclists. Naturalistic driving data based on pre‐extracted overtaking events with more other critical factors will be mined to create three models that cover four phases of an overtaking: approaching, overtaking, passing, and returning. These models will then be implemented as an automated overtaking prototype to a simulated platform for a motor vehicle to overtake bicyclists based on different strategies. An experiment of human study will be conducted to evaluate the prototype from both the viewpoints of the driver and the bicyclist that how they want to overtake and be overtaken safely. It is expected that the outcomes can offer the OEMs and suppliers who are keen on developing safe and human‐centered automated vehicle systems with useful insights. Furthermore, the insights can be helpful for legislation on the act or guidelines of protecting on‐road vulnerable bicyclists.
Research Mode: In Lab, Hybrid, Remote

UMTRI Project #7: Body Dimension Estimation from Clothed 3D scan
Faculty Mentor: BK-Daniel Park,


  • Proficiency in computer programming languages (C#, Python, etc.)
  • Interested in computer vision/machine learning research
  • Excellent oral and written communication skills
  • Ability to work well as a member of a team

Project Description: Three-dimensional (3D) surface measurement has become a central component of anthropometric surveys. Modern surface scanning equipment can accurately capture the shape of the surface of the body in a few seconds. However, the practical aspects of conducting 3D scanning surveys have changed little in the past decade. In particular, participants are required to change into close-fitting garb that minimizes the clothing effects on the subsequent scan. This clothing ensemble must be provided, along with suitable privacy for changing, and the consequence is that several seconds of scanning can require 10 minutes or more of preparation and considerable resources.

The University of Michigan Transportation Research Institute (UMTRI) recently introduced a new body shape estimation method, “Inscribed Fitting” (IF), that is much faster than previous techniques, requiring at most a few seconds of computation on a typical computer. This IF method uses an iterative process to estimate the body shape underlying the clothing, based on the observation that the correct body shape is the largest body shape that does not protrude through the clothing. The main objective of this study is to develop a standalone software system that can be used to estimate body shape, standard anthropometric dimensions, and body landmark locations from scan data obtained from individuals in arbitrary clothing ensembles.
Research Mode: In Lab, Remote, Hybrid

UMTRI Project #8: Identify and Testing User Interface Design Needs for AV-VRU communications
Faculty Mentor: Shan Bao,
Prerequisites: Team players who are motivated in working with other group members. Experience with conducting study to collect data from human participants are plus!
Project Description: For the vulnerable road user community and individuals (VRU, i.e., pedestrians and bicyclists), effective communication between Automated Vehicles (AV) and VRUs is crucial to VRUs developing trust as they interact with AVs. To support the effort of improving mobility and safety for all Americans, this study is d signed to address this issue. This project will collect user needs data and assess the information display efficiency and system suitability of accommodations to address common needs by using the principles of user-centered design to ensure that the prototypes have been developed to maximize the use of AVs by VRUs. By working on this project, you will have the opportunity to work on an exciting research topic and get connected with experts from leading companies in this domain. You will gain hands-on experiences working on instrumenting AVs at Mcity, and testing.
Research Mode: In-lab (Mcity testing), Online or Hybrid

UMTRI Project #9: A Tool for Augmented Reality (AR) Assisted Surgery: 3D Human Modeling and Visualization
Faculty Mentor: Jingwen Hu,
Prerequisites: Proficiency in computer programming languages (C#, C++, Python, etc.)
Project Description: An AR-assisted surgery tool will provide a composite view between computer-generated patient anatomy and a surgeon’s view of the operative field, which may lead to more precise understanding of the detailed anatomy and also significantly increase accuracy in tumor localization and resection. In this study, we will focus on a software tool that can address the rapid development of computer anatomy models and accurate registration between the anatomy model and real patient geometry, which are the two key aspects of AR-assisted surgery tools.  We plan to use an AR device, Microsoft HoloLens, as the main hardware to demonstrate the software capability, although our software should not be limited to HoloLens only.  In this study, we will use liver surgery as an example, thus the medical images and anatomy models will only focus on the liver and the surrounding tissues.  Because liver is the largest solid organ in the abdomen, is pliable, and operative interventions can alter its anatomy, it will pose significant challenges on model registration, which will be a good test for the AR-assisted surgery tool. For surgeons who have to deal with complex anatomical structures that are not always visible, the proposed AR-assisted surgery tool will provide much needed understanding of anatomic relations beneath the surface, and will likely lead to better accuracy, safer resection, lower complications, and superior surgical outcomes.
Research Mode: Online, Hybrid

UMTRI Project #10: Classification of dynamic driving maneuvers in naturalistic driving datasets
Faculty Mentor: Arpan Kusari,
Prerequisites: Expertise in SQL and Python is required; experience with Neural network model development is preferred
Project Description: Our goal through this project is to perform large scale automated data mining on naturalistic databases that UMTRI has collected and maintains. We want to automatically tag time points when any neighboring vehicles perform dynamic driving maneuvers such as cut-ins, turns, overtaking etc. The first step is to find any such event in a large scale dataset through SQL query into a training set. We will then use this training dataset to train a classifier to automatically crawl through the other datasets and tag events along with a confidence. The final product of the application will be a meta file which will have the event along with start and end times. The research output will be broken down into the following steps:

  • Get familiar with our database – 10%
  • For the cut-in maneuver, create a sample training dataset – 20%
  • Using the training dataset, train different available classification models and compare their performance – 30%
  • Run the classification models on some other unseen dataset – 20%
  • Choose some other maneuver and repeat – 20%

Research Mode: Online or hybrid

UMTRI Project #11: State of health monitoring and transmission accuracy assessment for road-side equipment employing Connected Vehicle Technology
Faculty Mentor: Jim Sayer,
Prerequisites: Driver’s License, Interest in Connected Vehicle Technology
Project Description: Over 75 Connected Vehicle devices are deployed at intersections, curves and mid-blocks crosswalks around Ann Arbor.  These devices can transmit information to approaching vehicles about the color and timing of traffic lights, or the presence of a pedestrian in the road ahead.  Of interest to researchers is the operational state of the Road-side Units (RSUs), as well as the accuracy of the transmissions being broadcast.  Currently, the signals are only used to provide warnings to drivers, but in the future automated vehicles may rely on accurate information being generated by these road-side units to navigate intersections safely.

In this study, students will use custom designed software and hardware while in the field to test the state of RSU’s around Ann Arbor and report the results.  Students will also be involved in helping to properly configure devices that are not properly functioning.  Work may also include tailoring pedestrian detection devices to more accurately detect crossing pedestrians, and tailoring in-vehicle warning parameters to best provide information to drivers about potential hazards including red-light violations.
Research Mode: Online data analysis with local driving around Ann Arbor

UMTRI Project #12: Short-term trajectory prediction with transformer networks and probabilistic machine learning model for autonomous vehicle planning
Faculty Mentor: Henry Liu,
Prerequisites: Proficient in Python; Interests and basic knowledge AI and Machine Learning; Some experience in Pytorch or Tensorflow is a plus
Project Description: Transformer network is an emerging neural network model that has received significant attention recently. This project uses transformer networks for short-term vehicle trajectory prediction, which is the core for sensor fusion, crash/near-crash warning, and many other applications. The student will work with my research group on the transformer and probabilistic model to create a probabilistic short-term trajectory predictor. Our preliminary results have already shown promising results. In this project we will design a more sophisticated model to challenge the state-of-art model results. This project is part of the Smart Intersection project that is sponsored by the USDOT.
Research Mode: Online or Hybrid

UMTRI Project #13: Modeling the Naturalistic Driving Environment for Evaluation of Automated Driving Systems
Faculty Mentor: Henry Liu,
Prerequisites: Proficient in Python; Interests and basic knowledge AI and Machine Learning; Some experience in Pytorch or Tensorflow is a plus
Project Description: To evaluate the safety performance of a highly automated driving system (ADS), computer simulation is arguably the most efficient and scalable method. Traditional microscopic traffic simulators, however, are mainly designed for traffic flow analysis. As a result, modeling of vehicles’ microscopic behaviors is not the focus and the interactions between vehicles are not well-captured in the model. For ADS testing and evaluation, the simulation environment should realistically characterize human driving behaviors in both normal (non-safety-critical) and safety-critical situations. This project will develop a data-driven approach based on deep learning to construct a naturalistic driving environment that is realistic, scalable, and computational-efficient. We will train and validate the performance of the proposed simulation environment using real-world naturalistic driving data. This project is partially funded by the Center for Connected and Automated Transportation.
Research Mode: Online or Hybrid

UMTRI Project #14: Urban traffic control and management with sampled vehicle trajectory data
Faculty Mentor: Henry Liu,


  • Proficiency in Python, GitHub
  • Interested in smart city/transportation, big data
  • Familiar with data processing, optimization, and basic machine learning methods

Project Description: Vehicle trajectory data collected from the connected vehicle provides new opportunities for traffic control and management, especially for traffic signal optimization. Different from conventional detector data, trajectory data could serve as a low-cost, continuous and reliable data source, which could advance conventional detector-based signal control to a detector-free signal control scheme. The goal of this study is to develop different algorithms to serve urban traffic control and management using the sampled vehicle trajectory data. We will focus on developing algorithms using either model-free machine learning methods or model-based optimization methods for the traffic estimation and control. Specifically, students will use real-world trajectory data as the input, use Python as the major programming language, to develop practical algorithms like lane-level trajectory data map matching, traffic volume estimation, and optimization algorithms.
Research Mode: Online or Hybrid

UMTRI Project #15: Support for Driver Interface Research
Faculty Mentor: Paul Green,
Prerequisites: none, but being a licensed driver is helpful
Project Description: We are conducting a variety of projects for which help is needed.  In support of a number of Army projects, we are writing a standard that defines measures of driving performance and provides representative data based on the literature and possibly based on original research.  We have developed an industry standard for this purpose in the past for cars and trucks driven on-road, but for this research, we need to include off-road vehicles and armored vehicles.  This research is quite fundamental in that it is defining the science of driving, but quite applied in that we need real-world data to support what we do.  In addition, anyone working in the group invariably becomes involved in other projects as well, if for no other reason than to provide a broader research experience.
Research Mode: In Lab (possibly), Online, Remote, Hybrid

UMTRI Project #16: Driving Simulator Development – Unreal Engine
Faculty Mentor: Paul Green,
Prerequisites: none, but being a licensed driver is helpful, knowledge of Unreal is helpful
Project Description: We have a number of projects with the U.S. Army related to driving combat vehicles.  In support of them, we need to develop a simulation in Unreal of driving in a specific virtual world, adding sound, vehicle dynamics, minimaps and a HUD to represent a particular vehicle.  We also need to record driving performance in real time.  We know this is feasible because a student completed elements of this in the past, but the documentation is incomplete and we need to add more features.  We have requested hardware for this task from the Army.
Research Mode: In Lab (possibly), Online, Remote, Hybrid

UMTRI Project #17: Continuing Development of a Manned Driving Simulator
Faculty Mentor: Paul Green,
Prerequisites: none, but being a licensed driver is helpful, knowledge of Python is helpful
Project Description: For almost 2 years, various MDP teams have been working on the development of a driving simulator that includes a moving base cab for studies of human interaction with partially automated and automated vehicles.  Our focus is on 3 elements: (1) a GUI to allow for the rapid creation of experiments (especially scenarios and vehicle placement), (2) the ability to import virtual worlds, and (3) control of a 2-DOF motion platform in real time (pitch and roll).  The underlying code runs under LINUX and uses CARLA and ROADRUNNER.
Research Mode: In Lab (possibly), Online, Remote, Hybrid

UMTRI Project #18: Development and Implementation of Software Tools for Human Centered Design
Faculty Mentor: Matt Reed,
Prerequisites: none, prior experience with R and/or Python will be helpful
Project Description: The Biosciences Group has developed a wide range of statistical models of human posture and body shape for use in human-centered design. However, the complexity of these models is such that relatively few people are able to use them. The goal of this project is to make more of these models available online for people around the world to use for human centered design. (As an example, see: The tools include interactive analysis of standard anthropometry (body dimensions), three-dimensional anthropometry, head and face geometry, and vehicle occupant postures. The student(s) will work with the faculty to develop and deploy online tools using the Shiny library in the R language. Applications may also be developed in Python for implementation in open-source tools such as FreeCAD and Blender3D.
Research Mode: In-person, Remote, or Hybrid

UMTRI Project #19: On-Demand Delivery Estimation and Routing
Faculty Mentor: Tayo Fabusuyi,
Prerequisites: Machine Learning; Programming languages (Python, R) and Data Visualization (e.g. GeoJSON)
Project Description: The Covid-19 pandemic has further underscored the importance of on-demand delivery services but getting actionable data on these deliveries has been problematic. We will be using publicly available microdata and proprietary data in providing estimates of these deliveries. This is with the objective of developing a nationally scalable approach that estimates the volume of these demand at fine geographic scales and that implements a routing algorithm that fulfills these delivery orders in the most societal beneficial manner.
Research Mode: Online or hybrid

UMTRI Project #20:  Vehicle Position-in-Lane: Ground Truth System
Faculty Mentor:  Dave LeBlanc
Prerequisites:  Programming experience.  Experience with Matlab and/or image processing is encouraged but not required.
Project Description:  UMTRI’s Engineering Systems Group uses experiments, simulations, and analytics to help industry and government sponsors (1) quantify the requirements of automated and semi-automated vehicles, and (2) design and demonstrate test methods to ensure that vehicles meet the requirements.   This project’s goal is to develop an automated processing pipeline for accurately determining the position of a vehicle within its lane using downward looking cameras.  The pipeline will consist of image processing the camera images and pushing the results to an SQL database for analyses, such as comparing these “ground truth” results to those that a prototype or production vehicle generates.  The student will interact with and be supported by the faculty mentor and experienced research engineers.
Research Mode: Hybrid (may include help with occasional hands-on testing)