Explainable AI, in the context of autonomous systems, like self driving cars, has drawn broad interests from researchers. Recent studies have found that providing explanations for an autonomous vehicle actions has many benefits, e.g., increase trust and acceptance, but put little emphasis on when an explanation is needed and how the content of explanation changes with context. In this work, we investigate which scenarios people need explanations and how the critical degree of explanation shifts with situations and driver types. Through a user experiment, we ask participants to evaluate how necessary an explanation is and measure the impact on their trust in the self driving cars in different contexts. We also present a self driving explanation dataset with first person explanations and associated measure of the necessity for 1103 video clips, augmenting the Berkeley Deep Drive Attention dataset. Additionally, we propose a learning based model that predicts how necessary an explanation for a given situation in real time, using camera data inputs. Our research reveals that driver types and context dictates whether or not an explanation is necessary and what is helpful for improved interaction and understanding.

中文翻译：

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解释或不解释：自动驾驶汽车解释必要性研究

可解释的人工智能，在自动驾驶系统的背景下，如自动驾驶汽车，引起了研究人员的广泛兴趣。最近的研究发现，为自动驾驶汽车的行为提供解释有很多好处，例如增加信任和接受度，但很少强调何时需要解释以及解释的内容如何随上下文变化。在这项工作中，我们调查了人们需要解释哪些场景以及解释的临界程度如何随情况和驾驶员类型而变化。通过用户实验，我们要求参与者评估解释的必要性，并衡量在不同情况下对他们对自动驾驶汽车的信任的影响。我们还提供了一个自动驾驶解释数据集，其中包含第一人称解释和 1103 个视频剪辑必要性的相关度量，扩充 Berkeley Deep Drive Attention 数据集。此外，我们提出了一个基于学习的模型，该模型使用相机数据输入实时预测对给定情况的解释的必要性。我们的研究表明，司机类型和背景决定了解释是否必要以及什么有助于改善互动和理解。