Project Gallery
We strive to share as much or our research as possible, with the hope the automotive industry can benefit from the findings of our safety advances. Explore our projects, and discover what we work on.

Passenger Response to Abrupt Evasive Maneuvers
Human Factors, Projects
This study observed volunteer passengers experiencing unexpected abrupt evasive maneuvers, including hard braking and swerving. Our goal was to understand passenger responses to abrupt vehicle maneuvers, to inform the development of onboard safety systems.

Driver Modeling in Transfers of Control From Conditional Automation
Human Factors, Projects
We conducted a naturalistic driver study to understand how drivers interact with automated systems in everyday commuting, examining periods where a driver is likely to be highly vigilant along with incidents where driving task may be in low demand.

Animal Pre-Collision System PCS Test Scenarios
Active Safety, Crash Avoidance, Projects
A study to develop testing protocols for automotive PCS designed to prevent and mitigate animal-related vehicle crashes by examining crash data, collecting and analyzing naturalistic driving data, and radar scanning deer in order to better establish test parameters.

Guidelines for Development of Evidence-Based Countermeasures for Risky Driving
Active Safety, Crash Data Analysis/Data & Analysis, Projects
With GuDEC, we’ve created guidelines for risky driving countermeasures based in evidence, guided by behavior change theories, and leading to sustained behavioral change in drivers who regularly engage in risky or unsafe driving.

Mental Models of ADAS/ADS Technologies: Impacts on Consumer Education and Vehicle Design
Human Factors, Crash Data Analysis/Data & Analysis, Projects
Our goal is to determine the nature of, and variants of, drivers’ mental models of ADAS/ADS technologies and apply research findings to consumer education and vehicle design.

Identifying Risk Mitigation and Modeling of Driver Behaviors in Scenarios of Varying Complexities
Human Factors, Projects
For this project, our goal is to identify the driving context in which risk mitigation behaviors do occur.

Surrounding Environment Recognition Technology and Evaluation Metrics
Active Safety, Crash Avoidance, Projects
Develop a deep learning based full-scene recognition of vehicle environment from a vision sensor. Examples are vehicles, pedestrians, bicyclists, traffic signs, buildings, curbs, etc.

Development of Testing Methods for Vehicle Road Departure Assist Systems
Crash Data Analysis/Data & Analysis, Projects
By analyzing over 25,000 high resolution images available on Google Street View across the country, we were able to determine that grass, metal guardrail, concrete divider and curbs are the most common roadside boundary objects.

Integrated Benefit Estimation
Crash Data Analysis/Data & Analysis, Projects
Estimate the Residual Safety Problem after Integrated Safety Systems (ISS) are deployed in the future. ISS consists of all active (auto braking for vehicle, pedestrian, bicyclist, lane keeping, etc.) and passive safety systems (advanced airbag, curtain shield airbag, roof strength, pedestrian protection active hood, etc.).

A Systems Approach to Interactions Between Driving Automation and People
Crash Data Analysis/Data & Analysis, Projects
Provide theoretical and mathematical framework of how drivers communicate at an intersection.