Evaluating Autonomous Vehicles’ Safety Benefits in Mixed Autonomy Scenarios

Connected autonomous vehicles (CAVs) are gradually advancing towards widespread deployments. CAVs promise to improve transportation safety by operating more efficiently and avoiding incidents like crashes due to human driver error. However, they may cause incidents themselves, especially when interacting with humans. The goal of this project is to evaluate the potential safety benefits of CAVs in mixed-autonomy settings, in which CAVs and human vehicles share the road. This work has three parts: (i) estimating the effective incident rates of CAVs and how they are distributed across a city, leading to algorithms for prioritizing incident responses so as to reduce their overall impact on traffic flow and safety; (ii) incorporating CAVs’ and human drivers’ ability to react to human pedestrians, leading to algorithms for CAVs to reduce pedestrians’ impact; and (iii) evaluating models and analysis in a mixed-autonomy simulator. Towards modeling CAVs’ effect on traffic incident rates, the research team will account for the fact that vehicle incident rates vary with the road congestion level and type, e.g., Pennsylvania data show that incidents are more common in heavy-traffic surface streets than sparsely populated highways. The team will build on their prior Mobility21 work studying mixed-autonomy traffic patterns to account for changes in congestion levels across the road network due to vehicle incidents, e.g., if CAVs overall reduce the incident rate on highways, this might lead to better overall traffic flow and fewer subsequent incidents. The results will enable prioritization of incident response so as to maximally reduce the resulting traffic congestion. The team will then incorporate the effects of human pedestrians into their mixed-autonomy setting. Pedestrians can change safety dynamics as their actions may be more difficult to predict, especially for CAVs that may not be well-trained on pedestrian data. For example, CAVs can improve traffic flow by more closely following other vehicles; this is less feasible when human pedestrians are present. The team therefore plans to incorporate these pedestrian “shocks” into their model of traffic flow and incident rates. The team will use these results to propose new techniques for CAVs to predict and plan for pedestrian behaviors. The team will use their existing mixed-autonomy simulator, developed with Mobility21’s support, to numerically evaluate their models and how the above safety effects vary for different amounts of CAVs. The team will also leverage models and feedback from their deployment partner, the Southwestern Pennsylvania Commission (SPC), in their simulations. The team will further measure how CAVs’ effects are distributed around a city and implications for equity (see also “Outputs” below). This project is synergistic with the concurrently submitted proposal entitled “Mitigating Cascading Failures for Safety in Transportation Networks in the era of Autonomous Vehicles,” where the goal is to evaluate the safety impact of AVs from the perspective of their impact on cascading road failures and congestion. In contrast, the current project focuses on CAVs’ safety impact in terms of the traffic incident rate in mixed-autonomy settings. As such, the two projects complement each other and can be combined at a total budget of $150,000 if preferred.


  • English


  • Status: Active
  • Funding: $201765
  • Contract Numbers:


  • Sponsor Organizations:

    Carnegie Mellon University

    Safety21 National UTC for Promoting Safety
    Pittsburgh, PA  United States  15213

    Office of the Assistant Secretary for Research and Technology

    University Transportation Center Program
  • Managing Organizations:

    Carnegie Mellon University

    Safety21 National UTC for Promoting Safety
    Pittsburgh, PA  United States  15213
  • Project Managers:

    Stearns, Amy

  • Performing Organizations:

    Carnegie Mellon University

  • Principal Investigators:

    Joe-Wong, Carlee

  • Start Date: 20230701
  • Expected Completion Date: 20240630
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01900232
  • Record Type: Research project
  • Source Agency: Safety21
  • Contract Numbers: 69A3552344811
  • Files: UTC, RIP
  • Created Date: Nov 20 2023 7:35PM