CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs

TL;DR

This paper presents a novel method using Heterogeneous Graph Neural Networks to generate realistic corner case scenarios from regular driving scenes, enhancing autonomous vehicle testing.

Contribution

Introduces a new approach that transforms scene graphs into corner cases using learned perturbations, improving data augmentation for autonomous vehicle safety validation.

Findings

Achieved 89.9% prediction accuracy in generating corner cases.

Successfully validated generated scenarios on baseline autonomous driving methods.

Demonstrated effective creation of critical situations for testing.

Abstract

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.