The Division of Data & Analytics (DDA) specializes in collaboration with industry, academic, and government partners to translate large-scale data collections into robust and timely guidance and decisions. The division focuses on challenging questions at the intersection of mechanical engineering, physics, computer science, statistics, behavior, performance, safety and policy. DDA projects leverage innovative data fusion approaches, algorithmic labeling processes, and interactive visualizations to translate disparate and highly dimensional data into visible progress and understandable results. The division's goals are to provide domain expertise and state-of-the-art data and analytic methods to enable our partners to answer their questions quickly, cost effectively, and with accessible output that is ready to address their most pressing needs.
- Applied Analytics
- Advanced Statistics
- Computer Vision & Machine Learning
- Data Annotation
- Data Applications
- Policy & Qualitative Data Analysis
Kevin Kefauver
Division Director
Gibran Ali
Applied Analytics
Matthew Casadonte
Program Manager
Feng Guo
Advanced Statistics
Julie McClafferty
Division Support
Miguel Perez
Data Applications
Abhijit Sarkar
Computer Vision & Machine Learning
Tammy Trimble
Policy & Qualitative Data Analysis
Stuart Walker
Data Annotation
- INDUSTRY BEST PRACTICES: VTTI's research findings helped inform SAE International's best practices for public safety and automated driving system-dedicated vehicles.
- DATA: The institute houses one of the largest repositories of continuous naturalistic driving data in the world and won a National Academy of Sciences-funded contract to make data from the largest naturalistic driving study ever conducted (the Second Strategic Highway Research Program Naturalistic Driving Study [SHRP 2 NDS]) available to researchers across the automotive and transportation industries.
- INNOVATION: The Automated Mobility Partnership provides members with access to a variety of real-world driving data and a suite of support tools focused on the development and evaluation of automated driving technologies.
- OUTREACH: Our division has conducted several studies focused on educating consumers. Active safety features, especially those associated with forward collision detection and avoidance technologies, have the potential to greatly reduce the number of serious and fatal crashes in the coming years. However, we recognize that consumers will need to be educated on the purpose, benefits, limitations, and proper use of these active safety technologies for this potential to be met.
- Estimating Crash Consequences for Occupantless Automated Vehicles
- Automated Vehicle Crash Rate Comparison Using Naturalistic Data
- FMVSS Considerations for Vehicles with Automated Driving Systems
- Human Factors Evaluation of Level 2 and Level 3 Automated Driving Concepts
- Naturalistic Study of Level 2 Driving Automation Functions
