The University of Michigan Transportation Research Institute (UMTRI) and the Michigan Economic Development Corporation (MEDC) partnered on an internship program which provides undergraduate students with practical research and work experience related to emerging careers. The overarching goal was to further the development of, and retain in Michigan, the necessary workforce to maintain the State’s leadership in Connected Vehicles, Autonomous Vehicle, and Mobility at large.
The program began in the Winter 2018 term with 10 University of Michigan students participating; 19 additional students from UM and Michigan Statue participated during the summer, 12 participated in Fall 2018, and 11 were in the program during Winter 2019. Projects provide skills related to analyzing real-world datasets, studying older driver behavior, improving human modeling efforts, developing websites and online tools, evaluating automated vehicle features, parametizing vehicle motion associated with motion sickness, coding traffic maneuvers from connected vehicle data, identifying the limitations of open source maps, and modeling ridesharing trends. Each term ends with a poster reception to highlight student research. Posters for each term are linked below.
Creating Data Schemas for IVBSS and SPMD Datasets
Development of Online Parametric Body Models
Evaluation of the AAATA Independent Wheelchair Securement Pilot Program
Guidelines for Development of Evidence-Based Countermeasures for Risky Driving
A Longitudinal Field Study of Trust and Acceptance of Driver Assistance Systems
Longitudinal Research on Aging Drivers
Pedestrian Bicyle Risk Exposure Visualization Tool
Security Analysis of ADAS and Automated Driving Systems
Towards a Notion of Context in Video Processing: Optimizing Neural Convolutional Networks
Head Tracking to Predict Motion Sickness in Passenger Vehicles
Using Head Depth to Parameterize Motion Sickness in Vehicles
Clustering IVBSS radar sensor data for surrounding vehicle detection
Cybersecurity: Vehicle Intrusion Detection
Developing a personalized Guardian system to assist aging drivers
Development of Canine Crash Test Dummies
Head Modeling for Concussion Assessment Considering Human Variability
Improving Map Visualization for Pedestrian Bicycle Website
Intrusion Detection on a CAN Bus
Longitudinal field study of trust and acceptance of advanced driver assistance systems
Longitudinal Research on Aging Drivers (LongROAD)
Medical decision making-application of transportation human factors knowledge
Head modeling for concussion assessment considering human variability
Simulating pre-existing rib fractures in the GHBMC finite element whole body model
Medical decision making-application of transportation human factors knowledge
Evaluating Methods for Estimating Traffic Volume for Unobserved Locations
Understanding Cyclist Travel Patterns and Predicting Route and Destination with Markov Models
A Statistical Method for Predicting Automobile Driving Posture
Lane-Marking Regulation for Automated Vehicles
Longitudinal Research on Aging Drivers (LongROAD)
Developing Statistical Geometry Models for Human Anatomy
A naturalistic bicycling study in the Ann Arbor area
Quantify & Detect Motion Sickness to Inform AV Design
Guidelines for Development of Evidence-Based Countermeasures for Risky Driving
Modeling Corner Case Scenarios Between Bicycles and Autonomous Vehicles
Simulation of Corner Case Scenarios Involving Cyclists and Vehicles: A MATLAB Application
Development of Accommodation Models for Soldiers in Vehicles
The Effect of Educational Video Games on the Learning of Practical Knowledge
Web Query and Visualization of Spatial Data
From a 2D VSSIM Traffic Simulation to a 3D Virtual Reality Traffic Experience
Comparing 2009 and 2017 National Household Travel Survey Results
Vehicle Motion Parametization to Evaluate Motion Sickness Susceptibility in AVs
Body Composition Prediction for Subject-Specific Models
World-Building for Educational Gameplay
Longitudinal Research on Aging Drivers (LONGROAD)
New Method to Rapidly Assess the Safety and Usability of an Automated Vehicle Driver Interface
Drivers' Safe Operation and Trust Calibration of Automated Vehicle Systems
Automated Vehicles: Driving Scenario Classification
Open Source Maps to Understand Real-World Vehicle Use and Safety
Simulation and Optimization of Dynamic and High-Occupancy Ridesharing Systems