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.

翻译：边界案例场景是测试和验证自动驾驶车辆安全性的重要工具。由于自然驾驶数据集中此类场景通常不足，使用合成边界案例增强数据极大地提升了自动驾驶车辆在特殊场景下的安全运行能力。然而，生成合成且逼真的边界案例仍是一项重大挑战。本研究提出一种基于异构图神经网络的新方法，将常规驾驶场景转化为边界案例。为此，我们首先将常规驾驶场景简洁表示为场景图，并对其结构和属性进行最小化操作。随后，我们的模型通过注意力机制与三元组嵌入学习扰动这些图以生成边界案例。输入图与扰动图被重新导入仿真环境，生成边界案例场景。我们的模型成功学会了从输入场景图生成边界案例，在测试数据集上达到89.9%的预测准确率。我们进一步在基准自动驾驶方法上验证了生成的场景，证明模型能够有效为基准方法创建关键情境。