Explainability is essential for autonomous vehicles and other robotics systems interacting with humans and other objects during operation. Humans need to understand and anticipate the actions taken by the machines for trustful and safe cooperation. In this work, we aim to enable the explainability of an autonomous driving system at the design stage by incorporating expert domain knowledge into the model. We propose Grounded Relational Inference (GRI). It models an interactive system's underlying dynamics by inferring an interaction graph representing the agents' relations. We ensure an interpretable interaction graph by grounding the relational latent space into semantic behaviors defined with expert domain knowledge. We demonstrate that it can model interactive traffic scenarios under both simulation and real-world settings, and generate interpretable graphs explaining the vehicle's behavior by their interactions.

中文翻译：

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扎根的关系推理：领域知识驱动的可解释的自主驾驶

可解释性对于自动驾驶汽车和其他机器人系统在操作过程中与人类和其他物体的交互至关重要。人类需要了解并预期机器采取的行动，以实现信任和安全的合作。在这项工作中，我们旨在通过将专家领域的知识纳入模型，在设计阶段实现自动驾驶系统的可解释性。我们提出了基础关系推理（GRI）。它通过推断代表代理人关系的交互图来建模交互式系统的基础动力学。通过将关系潜在空间置入由专家领域知识定义的语义行为中，我们确保了可解释的交互图。我们证明了它可以在模拟和实际设置下对交互式交通场景进行建模，