Task-Driven Controllable Scenario Generation Framework Based on AOG

Sampling, generation, and evaluation of scenarios are essential steps for intelligent testing of autonomous vehicles. Since uncertainty in driving behavior always leads to different occurrence frequencies of scenarios, we have to sample these scenarios in naturalistic datasets. Furthermore, a specified scenario needs to be further enriched and the driving behavior within it needs to be fully described to carry out generation in simulation systems. However, existing approaches generate scenarios randomly and uncontrollably, which makes them unable to precisely generate the specified scenarios. The driving behavior they describe is also memoryless and inflexible. To address the two issues, we propose a task-driven controllable scenario generation framework that can generate scenarios with the consideration of the driving behavior of Surrounding Vehicles (SVs) in a controllable manner. We first manually assign the driving behavior based on different testing tasks for all the considered vehicles. Then we expand the driving behavior temporally as the continuation and transition of several motion activities and generate the corresponding vehicle trajectories spatially. We adopt And-Or Graph (AOG) to model the transition between these motion activities. In contrast to the common memoryless Markov process, our framework generates driving behavior with continuity and driving memory. Finally, we evaluate our framework by generating lane-changing scenarios.