CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs
This work addresses the problem of insufficient corner cases in naturalistic driving datasets for autonomous vehicle safety validation, representing an incremental advancement in synthetic scenario generation.
The paper tackles the challenge of generating realistic corner case scenarios for autonomous vehicle testing by introducing a method that transforms regular driving scenes into corner cases using Heterogeneous Graph Neural Networks, achieving 89.9% prediction accuracy on a testing dataset.
Corner case scenarios are an essential tool for testing and validating the safety of autonomous vehicles (AVs). As these scenarios are often insufficiently present in naturalistic driving datasets, augmenting the data with synthetic corner cases greatly enhances the safe operation of AVs in unique situations. However, the generation of synthetic, yet realistic, corner cases poses a significant challenge. In this work, we introduce a novel approach based on Heterogeneous Graph Neural Networks (HGNNs) to transform regular driving scenarios into corner cases. To achieve this, we first generate concise representations of regular driving scenes as scene graphs, minimally manipulating their structure and properties. Our model then learns to perturb those graphs to generate corner cases using attention and triple embeddings. The input and perturbed graphs are then imported back into the simulation to generate corner case scenarios. Our model successfully learned to produce corner cases from input scene graphs, achieving 89.9% prediction accuracy on our testing dataset. We further validate the generated scenarios on baseline autonomous driving methods, demonstrating our model's ability to effectively create critical situations for the baselines.