Current technology for autonomous cars primarily focuses on getting the
passenger from point A to B. Nevertheless, it has been shown that passengers
are afraid of taking a ride in self-driving cars. One way to alleviate this
problem is by allowing the passenger to give natural language commands to the
car. However, the car can misunderstand the issued command or the visual
surroundings which could lead to uncertain situations. It is desirable that the
self-driving car detects these situations and interacts with the passenger to
solve them. This paper proposes a model that detects uncertain situations when
a command is given and finds the visual objects causing it. Optionally, a
question generated by the system describing the uncertain objects is included.
We argue that if the car could explain the objects in a human-like way,
passengers could gain more confidence in the car's abilities. Thus, we
investigate how to (1) detect uncertain situations and their underlying causes,
and (2) how to generate clarifying questions for the passenger. When evaluating
on the Talk2Car dataset, we show that the proposed model, \acrfull{pipeline},
improves \gls{m:ambiguous-absolute-increase} in terms of $IoU_{.5}$ compared to
not using \gls{pipeline}. Furthermore, we designed a referring expression
generator (REG) \acrfull{reg_model} tailored to a self-driving car setting
which yields a relative improvement of \gls{m:meteor-relative} METEOR and
\gls{m:rouge-relative} ROUGE-l compared with state-of-the-art REG models, and
is three times faster.

该论文提出了一种模型，用于检测当给定命令时不确定的情况和引起它们的视觉对象，并为乘客生成澄清问题，从而增强乘客对自动驾驶车辆的信心。