Our Work

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.

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.

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.

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.

For this project, our goal is to identify the driving context in which risk mitigation behaviors do occur.

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.

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.

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.).

Provide theoretical and mathematical framework of how drivers communicate at an intersection.

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