Current Research Opportunities
Join Us!
The SAFETY IMPACT Lab is always seeking individuals who are interesting in using crash data analysis to improve transportation on our roads! For perspective Masters or Doctorate students, please see the current research opportunities below. We also have positions open for undergraduate interns during the summer of 2022. For more details, see the job posting below.
2022 Summer Internship
We are seeking an undergraduate research assistant to perform vehicle crash reconstructions, crash test analysis, and occupant injury evaluation for Summer 2022. The research is conducted within the Center for Injury Biomechanics at Virginia Tech. The aim of our current project is to understand how the characteristics of road geometry, vehicle parameters, and driver state contribute to run-off-road crashes. We further seek to understand how these characteristics modify guardrail design and performance tests. The selected candidate will:
· Extract vehicle trajectories in real-world crashes
· Reconstruct crashes based on vehicle black boxes and deformation
· Perform statistical analysis on the collected data
· Assist with other research tasks as needed
Interest and experience in biomedical engineering, computer programming, and mechanics is desired. To be considered, interested applicants should submit a resume that includes GPA, and a cover letter via email to Dr. Riexinger. Compensation is $12/hour.
Crash and Injury Risk in Vehicles with Active Safety Systems
Up to 90% of car crashes are caused by driver error. US auto companies are introducing a radically new generation of cars onto US highways with advanced crash avoidance sensors/actuators – frequently referred to as Active Safety Systems. These systems can automatically brake and steer a car to avoid an impending crash. These are the first steps toward full automated, driverless cars. Current systems use forward looking cameras, millimeter-wavelength radar, and LIDAR to alert the driver of a crash and in some cases take over control of the car. Automated collision avoidance features on new production cars include automated radar braking, forward collision warning, lane departure prevention, blind spot detection, and adaptive cruise control.
Active safety systems promise potential reduction in crash injuries, however, these technologies may carry their own unique risks. In this project, we will couple laboratory vehicle test data with computational modeling to determine the crash risk and potential benefit of (1) the newest automated crash avoidance technologies, currently available only on luxury cars, (2) emerging technologies such as vehicle-to-vehicle communication, and (3) fully automated driverless cars.
Roadside Encroachment Database Development and Analysis
Over one-third of all traffic fatalities are single-vehicle run-off-road crashes. The design of roadside safety hardware depends on detailed data about all roadside encroachments even those that do not result in a crash. This project aims to develop a detailed dataset of these encroachments for passenger vehicles, motorcycles, large trucks. The encroachment data will be extracted from national crash databases, state crash databases, and naturalistic driving studies.
The road geometry, roadside slope, roadside obstacles, vehicle attributes, encroachment shape will be analyzed to understand their effect on barrier performance and occupant injury. Applications of this research could improve barrier testing procedures, understand differences between crash and non-crash encroachments, understand the difference between tracking and non-tracking encroachments, and determine encroachment differences between passenger vehicles, trucks, and buses.
Questions?
Contact Dr. Luke Riexinger