  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/
