UMTRI 2021 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: Data Elements from Video using Impartial Algorithm Tools for Extraction (DEVIATE) Faculty Mentor: Carol Flannagan, 
Prerequisites: Experience/knowledge of data structures, statistics, algorithm development, human cognition. Experience in Python, R, and/or C++
Project Description: In everything from autonomous vehicle testing to improving driver safety, video recordings of drivers provide important data, but to be usable, the data must first be extracted. Compared to human coders, automated algorithms have the potential to reduce expense and increase speed, while increasing the accessibility of the wealth of information captured about drivers’ behavior. Our project will focus on developing strategies for automating video data extraction to record vehicle occupant behavior, as well as objects and actions in the vehicle’s forward view. In all of these areas, there is a potential to introduce an unintended bias in the algorithms that could have negative societal implications. Measuring and reducing this bias will be a key goal of our work.
Research Mode: Online or hybrid 

UMTRI Project #3: 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, Remote, or Hybrid

UMTRI Project #4: Driving Scenario Simulation and Analysis 
Faculty Mentor: Shan Bao,
Prerequisites: Have coding experience/knowledge, and are comfortable to work with a big group.
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 real world driving scenarios in the virtual environment through software (e.g., Carla) 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) are available.
Research Mode: Online or Hybrid

UMTRI Project #5: 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, and develop hardware components to allow independent use of restraint systems. 
Research Mode: In lab, hybrid

UMTRI Project #6: 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, Remote, Hybrid

UMTRI Project #7: Assessment for Automated Driving Features
Faculty Mentor: Brian T. W. Lin,

  • will help conduct evaluation methods, collect subjective assessment data in a driving simulator, conduct data analytic methods, and analyze the data with statistical methods,
  • must have taken the classes of statistics, and human factors or usability assessment

Project Description: When driving with vehicles of automated features, information about the vehicle’s state and movement should be provided to the drivers, so they can stay in the loop of the system’s function. This project aims to evaluate how these features and provided information can be acceptable to the drivers. Methods for subjective assessment and usability testing will be developed. The experiment will take place in a driving simulator. Typical driving scenarios will be designed for the AV to engage, such as overtaking. We intend to collect drivers’ subjective assessment for the importance, usefulness, acceptability, annoyance, and so on, of the information presented through the interfaces. Suggestions will be provided to the sponsor about what information should be presented, when to present, how the information display could be improved, and how the interface could help build trust in the automated driving systems.
Research Mode: Online, Remote

UMTRI Project #8: 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 #9: 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.
Research Mode: Online or Hybrid

UMTRI Project #10: 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 #11: Development of a realistic simulation environment to train and test autonomous vehicles
Faculty Mentor: Arpan Kusari,
Prerequisites: Expertise in Python, and C++ is required; knowledge of robotics is desirable
Project Description: An essential component of the training and testing of decision-making of autonomous vehicles including the initial validation rests on having a simulation environment capable of producing vehicular data which rivals the complexity of real-world scenarios. However, the majority of current simulation environments utilize traffic flow simulation in order to generate and operate the surrounding vehicles which results in a huge gap between the learnt policy in the simulation environment and actual driving. We propose to develop an open source  simulation environment by incorporating human driver  behavior models and subsequent macroscopic intentions into the simulation environment. This simulation environment would utilize the naturalistic driving data already present at UMTRI in an “intelligent” manner to seed behaviors in the surrounding traffic. Research tasks for students include:

  • Research into the most popular traffic simulation environments as background – 10%
  • Create a simple simulation loop with basic motion models and ACC behavior for other vehicles with Python as front end and C++ as backend – 40%
  • Utilize extracted naturalistic driving data to seed traffic vehicle behavior – 20%
  • Add complex behavior for other vehicles utilizing intentions – 20%
  • Build/utilize other complex map layouts – 10%

Research Mode: Online or hybrid

UMTRI Project #12: 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 #13: Pedestrian Detection Using via Ultra-Wideband (UWB) Device for Smart Vehicles
Faculty Mentor: Henry Liu,
Prerequisites: Proficient programming skills, basic mathematics.
Project Description: Pedestrian detection is one of the critical problems in the autonomous driving field.

Traditional methods are either unreliable, e.g., a camera-based detection system, or expensive, e.g., a lidar-based detection system. As in 2019, Apple starts to put Ultra-Wideband (UWB) functionality into the iPhone; it is expected that shortly, most smartphones and other smart devices carried by humans will be equipped with UWB functionality. As the UWB device will give accurate localization results, it becomes an economical and precise method for pedestrian tracking and collision warning for smart vehicles. This project will develop a data-driven approach that detects and localizes pedestrians via UWB localization. If interested, the student can develop a cellphone app to illustrate the developed method.
Research Mode: Hybrid (online)

UMTRI Project #14: Designing a sustainable and resilient transportation system: Learning from our collective COVID 19 Experience
Faculty Mentor: Aditi Misra,
Prerequisites: Geospatial data analysis skills; Interest in travel behavior and equity; Familiarity with R and/or Python
Project Description: Natural disasters and disruptions disproportionately affect people from different socio-economic backgrounds. The experience with COVID provides a natural experimental setting for testing this hypothesis and in developing long term sustainable solutions for such disasters in future. This particular research project will focus on analyzing if and how transportation behavior is affected under and due to COVID related restrictions across the different segments of the society and the impact of such changes on future multimodal transportation planning, especially public transportation. 
Research Mode: Online, Remote