UMTRI 2020 SURE Research Projects

UMTRI Project #1: Adaptive Safety Designs for Injury Prevention: Human Modeling and Impact Simulations
Research Mentor Name: Jingwen Hu
Title: Research Associate professor          Email: jwhu@umich.edu
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.
Minimum qualifications of student:

  • Proficiency in scientific programming languages (Matlab, Python, or Mathematica, etc.)
  • Interested in injury/impact biomechanics research
  • Excellent oral and written communication skills
  • Ability to work well as a member of a team

Demonstrated ability in FE model development and application is a plus.

UMTRI Project #2: Data Elements from Video using Impartial Algorithm Tools for Extraction (DEVIATE) 
Research Mentor Name:
Carol Flannagan
Title: Associate Research Scientist          Email: cacf@umich.edu
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.
Minimum qualifications of students: Experience/knowledge of data structures, statistics, algorithm development, human cognition. Experience in Python, R, and/or C++.

UMTRI Project #3: Data Mining Mcity Level 4 Autonomous Shuttle Bus Data and Video Data Processing
Research Mentor Name: Shan Bao
Title: Associate Research Scientist          Email: shanbao@umich.edu
Project Description: This project is a Mcity funded project, which is designed to data mining the Mcity Autonomous Shuttle Bus data from real roads driving. Student interns get to work with the unique datasets on level 4 Autonomous vehicle when driving on real roads and will be able to answer a lot of interesting and important research questions through hands on experiences. We are looking for multiple student helpers who are excited about automated vehicle research.
Minimum qualifications of students: Have experience/knowledge of data structure (e.g., EECS 280 course), video data analysis and Processing, some coding skills (R, and/or Python) are preferable.

UMTRI Project #4: Driver State Monitoring for Level 3 Conditional Automation
Research Mentor Name: Monica L.H. Jones
Title: Assistant Research Scientist          Email: mhaumann@umich.edu
Project Description: With increasing automation (Level 2 (Partial Automation) to Level 3 (Conditional Automation)), the role of the driver will transition from Driver Driving (DD) to Driver Not Driving (DND) (NHTSA 2013).  Rather, the social contract in L3 automated driving is that the driver be ‘receptive’ to an automation take-over request (TOR) or vehicle system failure. Freed from completing basic, operational, and strategic tasks of driving, it is anticipated that drivers will have a much larger postural and behavioral repertoire, including non-nominal driving postures and extensive use of handheld devices and other activities. Driver monitoring systems (DSM) will be critical to facilitate effective driver vigilance and transitions between automated and manual driving. If the driver is only called upon occasionally, a driver’s level of preparedness to react and respond can be expected to vary and could degrade considerably due to, for example, vigilance decrements, or other driver state-related phenomena (e.g. distraction, fatigue/drowsiness, emotion). 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.  Data will be gathered on a closed test track facility. 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 and/or a TOR in L3 automated driving conditions. The results of this study may identify disallowed states and provide further design guidance for DSMs.
Minimum qualifications of student:

  • Some experience with scientific programming languages is required (e.g. Mathematica, MatLab, Python)
  • Familiarity with computer vision programming is desired
  • Ability to work well as a member of a team

UMTRI Project #5: Driving Scenario Simulation and Analysis
Research Mentor Name: Shan Bao
Title: Associate Research Scientist          Email: shanbao@umich.edu
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.
Minimum qualifications of students: Have coding experience/knowledge, and are comfortable to work with a big group.

UMTRI Project #6: Evaluation of Automated Wheelchair Tiedown and Occupant Restraint System
Research Mentor Name:  Kathleen D. Klinich
Title:  Associate Research Scientist          Email: kklinich@umich.edu
Project Description:  The purpose of this research project is to develop an automated wheelchair tiedown and occupant restraint system that could be used independently by people traveling in wheelchairs in an automated vehicle. During the summer of 2020, we will be finalizing different design concepts and testing protocols, conducting volunteer testing to assess the usability of the system, and using results from volunteer testing as input to modify designs.
Minimum qualifications of student Strong technical writing skills, experience with spreadsheet/data analysis, and an interest in improving user travel experience and working with people who have disabilities.

UMTRI Project #7: Motion Sickness to Inform Automated Vehicle Design
Research Mentor Name: Monica L.H. Jones
Title: Assistant Research Scientist          Email: mhaumann@umich.edu
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.
Minimum qualifications of student:

  • Some experience with scientific programming languages is required (e.g. Mathematica, MatLab, Python)
  • Familiarity with computer vision programming is desired
  • Ability to work well as a member of a team

UMTRI Project #8: User Interface Assessment for Automated Driving Systems
Research Mentor Name: Brian T. W. Lin
Title: Assistant Research Scientist          Email: btwlin@umich.edu
Project Description: In automated vehicles (AV), information will be provided through different interfaces to the drivers, so they can stay in the loop of the AV’s maneuvers and states. This project aims to evaluate these human-machine interfaces, which are visually shown on the windshield, dashboard, and center console, as well as the auditory information. Methods for subjective assessment will be developed. The experiment will take place on closed course test facilities. Typical driving scenarios will be designed for the AV to engage, such as overtaking, rerouting, taking exit ramp. During the scenarios, necessary information will be shown on the interfaces for the driver. We intend to collect drivers’ subjective assessment for the importance, usefulness, acceptability, annoyance, and so on, of the information presented through various interfaces. Suggestions will be provided to the sponsor about what information should be presented, how the interfaces could be improved, and how the interface could help build trust in the automated driving systems.
Minimum qualifications of student: The qualified student:

  • will help conduct evaluation methods, collect subjective assessment data on the test facilities, conduct data analytic methods, and analyze the data with statistical methods,
  • must have taken the classes of statistics, and human factors or usability assessment

UMTRI Project #9: Vehicle Occupant Behaviors across Automation Levels
Research Mentor Name: BK-Daniel Park
Title: Assistant Research Scientist          Email: keonpark@umich.edu
Description: Modern vehicle fleets are rapidly accepting vehicle automation technologies, generally categorized as level 0 (No automation) to 5 (Full automation). It has been reported that vehicle drivers and passengers tend to change their postures and vehicle interior configurations according to the automation levels. Several recent studies have proposed methods to track the out-of-position behaviors of vehicle occupants using various sensing technologies. These studies have primarily used either marker-based motion capture systems or multiple video cameras for tracking motions in vehicle. In 2017, a novel motion-capture system was developed at UMTRI that uses a single ToF depth sensor (Microsoft Kinect V2) to obtain naturalistic 3D motion data. In this study, we will classify occupant behaviors in AV using the motion capture system we developed. The overall objectives are to (1) investigate what kinds of occupant behaviors can be classified with automation levels of 0 to 3 from recorded posture data using a machine learning technique, (2) investigate preferences of the seat and vehicle interior configurations in each behavior, and (3) investigate what setups of sensing technologies are minimally necessary for reliable detection of the classified behaviors in various AV environments.
Minimum qualifications of student:

  • 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