Center for Injury Research and Prevention

Human Factors

Human factors engineering 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 research has examined behaviors, emotions, beliefs, and preferences of young drivers. Through the collection of objective evidence on how drivers handle traffic situations, intervention strategies for improving their knowledge and skills are able to be identified. The CIRP research team is also applying human factors to investigate the safety of children in self-driving vehicles.

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.
  • 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.