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/ 1 Introduction Researchers have studied the problem of understanding actions communicated through natural language in both simulated (Das et al., 2017; Gordon et al., 2017; Hermann et al., 2017) and real environments (Loghmani et al., 2018; de Vries et al., 2018; Anderson et al., 2017). This paper focuses on the latter. More concretely, we consider the problem in an autonomous driving setting, where a passenger can control the actions of an Autonomous V ehicle (A V) by giving natural language commands. We hereunder argue why this problem setting is particularly interesting.