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 specif...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2024-06, Vol.25 (6), p.6186-6199
Main Authors: Ge, Jingwei, Zhang, Jiawei, Chang, Cheng, Zhang, Yi, Yao, Danya, Li, Li
Format: Article
Language:English
Subjects:
AOG
Autonomous vehicles
Behavioral sciences
Controllability
intelligence testing
Lane changing
Markov processes
Scenario generation
Speed limits
Testing
Trajectory
Uncertainty
Vehicles
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3347535