Project Title: Evaluating Autonomous Vehicles’ Safety Benefits in Mixed Autonomy Scenarios

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. [...] Towards modeling CAVs’ effect on traffic incident rates, we 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. We will build on our prior Mobility21 work studying mixed-autonomy traffic patterns to account for changes in [...] We therefore plan to incorporate these pedestrian “shocks” into our model of traffic flow and incident rates. We will use these results to propose new techniques for CAVs to predict and plan for pedestrian behaviors. We will use our existing mixed-autonomy simulator, developed with Mobility21’s support, to numerically evaluate our models and how the above safety effects vary for different amo [...] This project is synergistic with our 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. [...] Outputs: Our concrete deliverables will be (i) algorithms for emergency responders to prioritize incident responseaccording to overall impact on congestion and safety in the road network; (ii) new algorithms that allow CAVs to plan for (potentially unexpected) human pedestrian behavior; (iii) quantitative estimates of how much CAVs will improve traffic flow for different types of roads, as a fu [...] We will work with our equity and deployment partner, the Southwestern Pennsylvania Commission (SPC), to ensure the usefulness of our project outputs, through regular meetings for feedback. [...] We expect that this feedback will allow us to account for metrics that are important for all communities affected by CAV deployments, helping to ensure that we address equity concerns associated with CAV deployments. Outcomes/Impacts: By quantifying CAVs’ effects on traffic flow and accident rates with other vehicles and human pedestrians, we will enable regulatory agencies to anticipate the [...] Similar to our prior "Big Idea" project with Mobility21, on which this project builds, our work could inform the policies that agencies may wish to take in regulating CAVs (e.g., developing new metrics for how well they interact with human drivers and pedestrians). It will further provide quantitative validation for (or evidence against) claims that CAVs can benefit traffic and traffic safety a [...] Finally, our simulator may aid such agencies and other researchers in studying CAV and human behavior in mixed-autonomy systems. Emergency responders may benefit from our work by prioritizing responses to incidents that have the greatest overall impact (in terms of traffic congestion and follow-on incidents) on the road network. [...] CAV manufacturers may also be able to benefit from this work, as it will help them quantify the importance of developing AVs that interact “well” with humans and design learning algorithms that help CAVs to do so. These new learning techniques may enhance the benefits of CAVs and increase their potential for deployment.