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信息编码为人类定义的概念集，已在可解释机器学习领域得到成功应用。在人机协同或自动驾驶场景中，可解释性模型能够帮助用户理解和接纳自动驾驶系统的决策，从而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行为解释。通过构建人类可理解的概念层，我们可在学习车辆控制指令的同时解释连续驾驶场景。该方法能够判别人类驾驶员（或自动驾驶系统）对目标间隙或转向指令的偏好变化，究竟源于外部刺激还是内在偏好改变。在保持与潜在视觉特征同等竞争力的前提下，我们的模型框架显著提升了可解释性。