Traffic Sign Interpretation in Real Road Scene

TL;DR

This paper introduces a new task called Traffic Sign Interpretation (TSI) that aims to understand the global semantic relationships among traffic signs in real scenes and translate them into natural language to improve autonomous driving accuracy.

Contribution

The paper proposes a multi-task learning architecture for TSI, creates a new dataset TSI-CN with real road scene images and annotations, and demonstrates the effectiveness of the approach in complex traffic scenarios.

Findings

TSI task is feasible with the proposed architecture.

The TSI-CN dataset supports training and evaluation of TSI models.

The model can interpret complex traffic sign semantics successfully.

Abstract

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.