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Our Work

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.

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.

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Photo of a car approaching a deer.

Animal Pre-Collision System PCS Test Scenarios

Crash Avoidance, Active Safety, 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.

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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.

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Illustration of car evaluating environment.

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.

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Illustration of car.

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.

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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.

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Computer analyzed image of the road objects.

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.

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Photo of a city with busy traffic at night.

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.).

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Photo of a stop light in a busy city.

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.

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Photo of woman having a relaxing car ride.

Naturalistic Observations and Simulation of Communication Between Road Users

Human Factors, Human Technology Integration, Projects

Identify what kind of communication we have with other road users (e.g., pedestrians, other vehicles) with cutting-edge technology of computer vision.

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