Driving Simulator Based Interactive Experiments: Understanding Driver Behavior, Cognition and Technology Uptake under Information and Communication Technologies

Advanced Traveler Information Systems (ATIS) and in-vehicle information systems (IVIS) are becoming an integral part of the current driving experience. Although information through in-vehicle technologies provides assistance to drivers with diverse travel-related information (for example, real-time traffic information, weather forecast, and warning and emergency alerts), it also entails additional cognitive workload that can cause safety hazards and behavioral inconsistencies, especially if the information delivery mechanism is not well-designed. Thus, understanding the impacts of real-time information from multiple sources (such as variable message signs, GPS, radio, etc.) on drivers’ cognition and its effects on the decision-making process is essential for designing futuristic IVIS. In addition, it is desired that a driver would fully comply with such information to improve transportation system performance. In this study, the authors develop interactive driving simulator experiments to understand the relationship of drivers’ physiological data on their perceptional and psychological states as well as their revealed route choices. These experiments use a real road network from Indianapolis, Indiana, for which participants determine route preferences based on real-time information provision as well as route attributes (e.g., freeway, number of turns, stops, length, and so on). Various information scenarios with multiple disseminating sources are prepared to examine participants’ perceptional and psychological states depending on different information characteristics (e.g., amount, source, or content). High-definition cameras and biosensors (i.e., electroencephalography, electrocardiography, and eye tracker) are integrated with the driving simulator experiments to observe participants’ physiological data. The realtime coordination between the multiple biosensors, high-definition cameras, and driving scenarios enables to understand drivers’ dynamic cognitive states during the driving period depending on the presented cues (such as real-time travel information). Based on the data collected, the authors develop behavior models to investigate the impacts of cognitive effects induced by real-time traffic information along with situational factors (such as trip purpose and traffic congestion), real-time travel information characteristics (such as amount, content and source) and individual driver characteristics (such as age, gender and education) on the driver route choice decision-making process.


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Figures; Maps; Photos; References; Tables;
  • Pagination: 39p

Subject/Index Terms

Filing Info

  • Accession Number: 01667756
  • Record Type: Publication
  • Report/Paper Numbers: NEXTRANS Project No. 143PUY2.1
  • Contract Numbers: DTRT12-G-UTC05
  • Created Date: Mar 15 2018 10:15AM