Driver-Pedestrian Perceptual Models Demonstrate Coupling: Implications for Vehicle Automation

Developing vehicle automation that accommodates other road users and exhibits familiar behaviors may enhance traffic safety, efficiency, and fairness, leading to tolerance of the technology. However, the interdependence between vehicle automation and other road users makes them more challenging than typical control and path planning tasks. Through the lens of joint activity theory, we model driver and pedestrian behavior to explore how they balance and negotiate competing risk and velocity goals through movement. Joint activity theory informs an interpretation of these movements as signals, which can be associated with perceptual processes. We use simulation-based inference to estimate parameters of coupled driver and pedestrian perceptual models using naturalistic driving data. Perceptual models provide links between the processes guiding evaluation of risk and velocity maintenance, and how they govern driver acceleration and pedestrian walking. We found that the coupled simulations describe how drivers adjust their yielding behavior in the face of pedestrian risk, and how risk affects pedestrians' decisions to cross. Dynamic risk and velocity parameters predicted safety, efficiency, and fairness outcomes, suggesting that the parameters and their dynamic perceptual models describe important components of the interactions. Traditional approaches employ static, summary predictors, which may fail to capture their continuous evolution and negotiation over time. Dynamic models of the interaction between drivers and pedestrians can inform vehicle automation by identifying deviations from communication norms, extracting interaction features, and evaluating communication and coordination.

Automated summary

By using joint activity theory, this study explores how drivers and pedestrians balance and negotiate competing risk and velocity goals during movement. Simulation-based inference is used to estimate parameters of coupled driver and pedestrian perceptual models. It is found that dynamic risk and velocity parameters predict safety, efficiency, and fairness outcomes, providing guidance for vehicle automation.