Concept bottleneck models have been successfully used for explainable machine learning by encoding information within the model with a set of human-defined concepts. In the context of human-assisted or autonomous driving, explainability models can help user acceptance and understanding of decisions made by the autonomous vehicle, which can be used to rationalize and explain driver or vehicle behavior. We propose a new approach using concept bottlenecks as visual features for control command predictions and explanations of user and vehicle behavior. We learn a human-understandable concept layer that we use to explain sequential driving scenes while learning vehicle control commands. This approach can then be used to determine whether a change in a preferred gap or steering commands from a human (or autonomous vehicle) is led by an external stimulus or change in preferences. We achieve competitive performance to latent visual features while gaining interpretability within our model setup.

翻译：概念瓶颈模型通过将人类定义的概念编码到模型信息中，已在可解释机器学习领域取得显著成功。在人机协同或自主驾驶场景中，可解释性模型有助于提升用户对自动驾驶系统决策的接受度与理解，这些决策可用于合理化解释驾驶员或车辆行为。我们提出一种新方法，将概念瓶颈作为视觉特征用于控制指令预测及用户与车辆行为解释。通过学习可理解的语义概念层，我们既能解释连续驾驶场景，又能同步学习车辆控制指令。该方法可判断人类（或自动驾驶系统）在变道偏好或转向指令上的变化，究竟是源于外部刺激还是偏好迁移。相较于隐式视觉特征，我们的模型在保持可解释性的同时取得了与之相当的竞争性能。