Generation of critical pedestrian scenarios for autonomous vehicle testing

Current autonomous vehicle (AV) testing scenarios predominantly focus on interactions between AV and surrounding vehicles, with limited consideration given to high-risk pedestrian interactions. This paper presents a method for generating critical test scenarios specifically designed for pedestrian-oriented evaluations. First, microscopic traffic data were collected from 12 signalised intersections in 4 cities across China. By extracting overlapping vehicle and pedestrian trajectory data within the same spatiotemporal context, a vehicle–pedestrian interaction scenario library was created. Second, a three-stage autonomous emergency braking model was used to simulate the decision-making and control processes of AV, replacing the vehicle agency in the original scenario library. In addition, the artificial potential field method was applied to assess real-time interaction risks, enabling the identification of high-risk scenarios. A pedestrian-oriented critical test scenario generation framework was then developed, defining key decision variables such as speed differences, relative lateral distances, and relative longitudinal distances between pedestrians and vehicles. An importance sampling function, incorporating both scenario exposure frequency and interaction risk, was designed to generate critical scenarios. The process was further refined with an auxiliary objective function to guide the search direction. To improve computational efficiency, swarm optimisation and flood-fill algorithms were employed. Using this method, 50 high-value vehicle–pedestrian interaction test scenarios, characterised by high exposure frequency and risk, were generated. These scenarios encompass diverse and high-risk interaction dynamics, providing robust support for high-fidelity pedestrian safety testing of AV.