The adoption of autonomous vehicles in urban areas necessitates thorough safety validation and assurance, particularly in response to the complex behaviors of pedestrians. Current testing methods involve simulations that create challenging and risky scenarios for autonomous driving systems. However, these simulations are overly simplistic and do not reflect the diverse and complex nature of real-world pedestrian behaviors and situations. This inadequacy hinders effective safety validation and reporting.

To address this gap, this work aims to systematically define pedestrian behaviors on the road, highlighting the need for more realistic simulation models. It identifies key pedestrian types and proposes a pedestrian behavior ontology to improve simulation efficacy and facilitate better communication among stakeholders in the autonomous vehicle industry.

Modeling rich and diverse pedestrian behaviors and scenarios is challenging and requires integrating various methods. This work introduces a multi-state pedestrian model, where each state captures specific behaviors, including essential micro-behaviors like stopping in the middle of the road. Scenario-based testing often fails to produce long-tail (rare) pedestrian and traffic scenarios. To address this, the work proposes RePed, a novel method to reproduce and adapt long-tail scenarios in simulations. Additionally, it introduces HyGenPed, a system to procedurally generate diverse pedestrian routes in polymorphic crosswalk areas, enhancing real-world diversity in simulations.

In summary, the work outlines a clear approach for defining and modeling real-world pedestrian behaviors and scenarios for simulation-based testing of autonomous vehicles, aiming to make test results more interpretable, reliable, and communicable. It presents innovative methods to achieve these goals.