Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the worst-case scenarios based on known attacks is not comprehensive, not to mention that some of them even rarely occur in the real world. In addition, the distribution of safety-critical data is usually multimodal, while most traditional attacks and evaluation methods focus on a single modality. To solve the above challenges, we propose a flow-based multimodal safety-critical scenario generator for evaluating decisionmaking algorithms. The proposed generative model is optimized with weighted likelihood maximization and a gradient-based sampling procedure is integrated to improve the sampling efficiency. The safety-critical scenarios are generated by querying the task algorithms and the log-likelihood of the generated scenarios is in proportion to the risk level. Experiments on a self-driving task demonstrate our advantages in terms of testing efficiency and multimodal modeling capability. We evaluate six Reinforcement Learning algorithms with our generated traffic scenarios and provide empirical conclusions about their robustness.

中文翻译：

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用于决策算法评估的多模式安全关键场景生成

现有的基于神经网络的自治系统被证明容易受到对抗性攻击，因此对其稳健性的复杂评估非常重要。然而，仅基于已知攻击来评估最坏情况下的鲁棒性并不全面，更何况其中一些甚至很少发生在现实世界中。此外，安全关键数据的分布通常是多模态的，而大多数传统的攻击和评估方法都集中在单一模态上。为了解决上述挑战，我们提出了一种基于流的多模式安全关键场景生成器，用于评估决策算法。所提出的生成模型通过加权似然最大化进行了优化，并集成了基于梯度的采样程序以提高采样效率。安全关键场景是通过查询任务算法生成的，生成场景的对数似然与风险级别成正比。自动驾驶任务的实验证明了我们在测试效率和多模态建模能力方面的优势。我们用我们生成的交通场景评估了六种强化学习算法，并提供了关于它们鲁棒性的经验结论。