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.
Non-Driving Occupant Posture and Activities in Moving Vehicles
Human Factors, Projects
The objective of this project was to increase the scientific understanding of typical front-seat passenger postures through a naturalistic study observing in-vehicle behaviors.
Motion and Muscle Activation of Young Volunteers in Evasive Vehicle Maneuvers
Human Factors, Projects
The objective of this study was to quantify key adult and child occupant kinematic, kinetic and muscle responses from sudden evasive vehicle maneuvers. This data is useful to develop future tools to better assess injury risk in crashes preceded by crash avoidance.
Development of Standard Bicyclist Crash Scenarios
Crash Data Analysis/Data & Analysis, Projects
Naturalistic driving data was collected from 110 drivers over the course of a year capturing vehicle telematics data and camera footage, to better understand how bicyclists and cars interact and identify dangerous events.
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.