Investigation of Driver Adaptations in a Mixed Traffic Environment

Existing mathematical models for car-following are mostly descriptive and do not inherently estimate behavioral responses due to different traffic conditions, such as changes in roadway, environment, or vehicle conditions. These models include behavioral parameters (e.g., reaction time, degree of aggressiveness, etc.), which are calibrated with aggregate data collected under various traffic conditions. However, these models fail to capture changes in driver behavior caused by changes in the driving environment and thus fail to address vehicle interactions and the mechanisms that lead to breakdown phenomena. This issue becomes more apparent with the emergence of vehicle automation and advanced vehicle technologies as these directly impact the driving task. Through automation, drivers do not have immediate or direct control of their speed; therefore, task demand is expected to be different. Since speed modifications are expected to be slower with automation, it may be challenging to control driver’s workload level. Automation constrains driver capability through slower reaction times, information-processing capacity and speed. Driver’s activation (arousal) level is diminished, and drivers are more prone to be distracted. As we transition to partially automated or fully automated systems, the development of models that incorporate explanatory psychological constructs will be crucial. This project builds on the research team's previous work (Modeling Driver Behavior and Driver Aggressiveness Using Biobehavioral Methods – PART I, II, and III) where the team developed an extension to the Intelligent Driver Model (IDM) for manual driving, which captures three cognitive parameters: workload, situation awareness, and level of activation. The objective for this research project is to assess car-following behavioral changes of these cognitive parameters due to vehicle automation and build a framework for capturing these changes in a car-following model (e.g., the IDM).

Language

• English

Project

• Status: Active
• Funding: $402042
• Contract Numbers:

  69a3551747107

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Mid-America Transportation Center

  University of Nebraska-Lincoln
  2200 Vine Street, PO Box 830851
  Lincoln, NE  United States  68583-0851
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Kansas, Lawrence

  1530 West 15th Street
  Lawrence, KS  United States  66045
• Principal Investigators:

  Kondyli, Alexandra

  Chrysikou, Evangelia

• Start Date: 20230213
• Expected Completion Date: 20240630
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Alertness; Autonomous vehicles; Behavior; Car following; Drivers; Vehicle mix
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01908348
• Record Type: Research project
• Source Agency: Mid-America Transportation Center
• Contract Numbers: 69a3551747107
• Files: UTC, RIP
• Created Date: Feb 17 2024 4:29PM