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 autonomous vehicles' actions has many benefits
(e.g., increased trust and acceptance), but put little emphasis on when an
explanation is needed and how the content of explanation changes with driving
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 self-driving cars in
different contexts. Moreover, we present a self-driving explanation dataset
with first-person explanations and associated measures of the necessity for
1103 video clips, augmenting the Berkeley Deep Drive Attention dataset. Our
research reveals that driver types and driving scenarios dictate whether an
explanation is necessary. In particular, people tend to agree on the necessity
for near-crash events but hold different opinions on ordinary or anomalous
driving situations.

研究证明，提供自动驾驶汽车行动解释具有许多优点，但人们需要什么样的解释及其关键程度在不同的驾驶情境下发生转变。该研究通过实验发现，驾驶者类型和驾驶场景决定是否需要解释，特别是在危机事件发生附近，人们往往同意必须提供解释。