CIRP researchers use driving simulation to study human factors involved in driving safety.
Human factors research involves the study of factors and development of tools that facilitate the human interaction with systems in a safe and efficient way. Human factors best practices, such as user-centered design, call for including potential users of novel products or processes early and often throughout development efforts. At CIRP, Human Factors researchers are examining behaviors, emotions, beliefs, and preferences of young drivers and collecting objective evidence on how drivers handle traffic situations in order to develop intervention strategies for improving their knowledge and skills.
Exemplar CIRP Projects Involving Human Factors Methodology:
- Ensuring Safety of Children in Self-Driving Vehicles
This study seeks to understand the safety needs of children riding unaccompanied in self-driving vehicles. Many parents rely on services like Uber to bring their children, riding unaccompanied by a parent or caregiver, to afterschool activities or other functions. As self-driving cabs are now entering the roadways, the question about the proper age for a minor to be unaccompanied in a cab becomes more complex. This research will help inform specification of safety features, guidelines, and policies that will enable children to safely ride unaccompanied in self-driving vehicles.
A new report from SafeKids Worldwide, Children in Autonomous Vehicles Blue Ribbon Panel Report, provides recommendations to automakers and policymakers. CIRP Human Factors Researcher Patrice D. Tremoulet, PhD served as a member of the panel and contributed to the recommendations. Read a blog post about the report.
- Emergency Autonomous to Manual Takeover in Driving Simulator: Teens vs. Adult Drivers
This study uses CIRP’s advanced driving simulator to safely introduce teen and adult drivers to driving in an autonomous vehicle and assess their ability to remain vigilant and promptly take over in the case of a failure of the autopilot. The project aims to understand how much driving experience is needed to safely take over from autopilot mode, as well as how the driver’s age influences the ability to sustain attention. This information can be used to better understand the human factors at play in self-driving technology.
- Exploration of the Effect of Positive Reinforcement on Teen Driving Behavior
In-vehicle monitoring systems offer the potential to improve safety by generating alerts and positive feedback when certain driving practices are detected. This study aimed to understand the effect of this type of positive reinforcement on the shaping of teen and youth driving behaviors by collecting on-road data and simulator-based driving performance.
- Understanding and Predicting Human Driving Behaviors via Machine Learning Models
This multi-year projected utilized experimental and analytical techniques to create accurate models of teen drivers’ behavior to inform the development and testing of new technology and training methodologies to improve teen driving and reduce risk. The broad objective was to examine the potential for the personalized feedback to improve driving behavior and reduce dangerous behavior, specifically in the context of speed management of teen drivers.
- Effect of Distraction on Teen Driving Performance in an Emotionally Realistic Driving Simulator
This study aimed to create in-vehicle stressful tasks to distract teen drivers while measuring teens' abilities to handle environmental stressful events and to measure teens' beliefs and behaviors about in-vehicle distractions by measuring their risk-taking and decision-making characteristics.
CIRP/Villanova Child Pedestrian Safety Research Team (left-right): Catherine Krawiec, Robert Fralinger, Aditya Belwadi, PhD, and Seri Park, PhD with LadyBug 360-degree camera.
Virtual Reality, Eye-Tracking, and Pedestrian Safety: Where Do Pedestrians Look Before They Cross? To determine where child pedestrians really look before they cross the street, CIRP researchers recently conducted a one-of-a-kind pilot study with colleagues from Villanova University. They employed a virtual pedestrian crossing environment to explore how eye-tracking technology could be used to evaluate children’s behavior and point of view before crossing.
Their findings, recently presented as a poster at the Biomedical Engineering Society Annual Meeting, were published by the Transportation Research Board in January 2019. Ultimately, the researchers hope that their work in developing this human factors methodology will pave the way for future studies to reveal trends in how visual focus patterns can predict crossing behavior and safety.
Read a blog post about Human Factors research.