8 research outputs found
Giving Commands to a Self-Driving Car: How to Deal with Uncertain Situations?
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 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.Comment: Accepted in Engineering Applications of Artificial Intelligence
(EAAI) journa
Talk2Car: Taking Control of Your Self-Driving Car
A long-term goal of artificial intelligence is to have an agent execute
commands communicated through natural language. In many cases the commands are
grounded in a visual environment shared by the human who gives the command and
the agent. Execution of the command then requires mapping the command into the
physical visual space, after which the appropriate action can be taken. In this
paper we consider the former. Or more specifically, we consider the problem in
an autonomous driving setting, where a passenger requests an action that can be
associated with an object found in a street scene. Our work presents the
Talk2Car dataset, which is the first object referral dataset that contains
commands written in natural language for self-driving cars. We provide a
detailed comparison with related datasets such as ReferIt, RefCOCO, RefCOCO+,
RefCOCOg, Cityscape-Ref and CLEVR-Ref. Additionally, we include a performance
analysis using strong state-of-the-art models. The results show that the
proposed object referral task is a challenging one for which the models show
promising results but still require additional research in natural language
processing, computer vision and the intersection of these fields. The dataset
can be found on our website: http://macchina-ai.eu/Comment: 14 pages, accepted at emnlp-ijcnlp 2019 - Added Talk2Nav Referenc