Adaptive Pedestrian Agent Modeling for Scenario-based Testing of Autonomous Vehicles through Behavior Retargeting

Abstract

This work proposes a new representation of pedestrian crossing scenarios and a hybrid modeling approach, RePed, that facilitates transferring microscopic behavior models from behavior research to higher-level trajectories. With this, real-world trajectory-based scenarios can be augmented with a diverse set of human crossing maneuvers, producing a wealth of new scenarios and addressing the scarcity of rare case data that existing works struggle to deal with. Leveraging the controllability of this modeling approach, perturbation-based augmentation can be applied to enrich scenarios further. In addition, the representation is rooted in the Ego vehicle’s coordinate system with a logical representation of roads. This design enables scenario retargeting to various road structures, traffic conditions, and ego vehicle behaviors. Thus, it strongly supports scenario-based testing by forcing pedestrians to produce certain situations in simulation even when the Ego Vehicle tries to evade them.