A systematic study of traffic sign recognition and obstacle detection in autonomous vehicles

Abstract

Purpose

This study aims to compare traffic sign (TS) and obstacle detection for autonomous vehicles using different methods. The review will be performed based on the various methods, and the analysis will be done based on the metrics and datasets.

Design/methodology/approach

In this study, different papers were analyzed about the issues of obstacle detection (OD) and sign detection. This survey reviewed the information from different journals, along with their advantages and disadvantages and challenges. The review lays the groundwork for future researchers to gain a deeper understanding of autonomous vehicles and is obliged to accurately identify various TS.

Findings

The review of different approaches based on deep learning (DL), machine learning (ML) and other hybrid models that are utilized in the modern era. Datasets in the review are described clearly, and cited references are detailed in the tabulation. For dataset and model analysis, the information search process utilized datasets, performance measures and achievements based on reviewed papers in this survey.

Originality/value

Various techniques, search procedures, used databases and achievement metrics are surveyed and characterized below for traffic signal detection and obstacle avoidance.

Citation

Vartak Koli, R.D. and Sharma, A. (2024), "A systematic study of traffic sign recognition and obstacle detection in autonomous vehicles", International Journal of Intelligent Unmanned Systems, Vol. 12 No. 4, pp. 399-417. https://doi.org/10.1108/IJIUS-03-2024-0065