Most existing traffic sign-related works are dedicated to detecting and
recognizing part of traffic signs individually, which fails to analyze the
global semantic logic among signs and may convey inaccurate traffic
instruction. Following the above issues, we propose a traffic sign
interpretation (TSI) task, which aims to interpret global semantic interrelated
traffic signs (e.g.,~driving instruction-related texts, symbols, and guide
panels) into a natural language for providing accurate instruction support to
autonomous or assistant driving. Meanwhile, we design a multi-task learning
architecture for TSI, which is responsible for detecting and recognizing
various traffic signs and interpreting them into a natural language like a
human. Furthermore, the absence of a public TSI available dataset prompts us to
build a traffic sign interpretation dataset, namely TSI-CN. The dataset
consists of real road scene images, which are captured from the highway and the
urban way in China from a driver's perspective. It contains rich location
labels of texts, symbols, and guide panels, and the corresponding natural
language description labels. Experiments on TSI-CN demonstrate that the TSI
task is achievable and the TSI architecture can interpret traffic signs from
scenes successfully even if there is a complex semantic logic among signs. The
TSI-CN dataset and the source code of the TSI architecture will be publicly
available after the revision process.

我们提出了一个交通标志解释（TSI）任务，旨在将全局语义相关的交通标志（例如驾驶指令相关的文本、符号和指引面板）翻译成自然语言，为自动驾驶或辅助驾驶提供准确的指导支持。同时，我们设计了一个多任务学习架构来完成 TSI，负责检测和识别各种交通标志，并将其解释成类似人类的自然语言。在中国公路和城市道路的真实场景图像上进行的实验表明，即使标志之间存在复杂的语义逻辑，TSI 任务也是可实现的，而 TSI-CN 数据集和 TSI 架构的源代码将在修订过程后公开发布。