Development of Open Informal Dataset Affecting Autonomous Driving

TL;DR

This paper details the creation of a large, open dataset of road objects and dynamic scenes under diverse conditions to support autonomous driving technology development.

Contribution

It introduces a comprehensive methodology for data collection, annotation, and processing, resulting in a substantial dataset for object recognition in self-driving cars.

Findings

Collected 100,000 images of road objects and pedestrians.

Gathered 200,000 images of police and safety personnel.

Compiled a dataset of 5,000 annotated images for training and evaluation.

Abstract

This document is a document that has written procedures and methods for collecting objects and unstructured dynamic data on the road for the development of object recognition technology for self-driving cars, and outlines the methods of collecting data, annotation data, object classifier criteria, and data processing methods. On-road object and unstructured dynamic data were collected in various environments, such as weather, time and traffic conditions, and additional reception calls for police and safety personnel were collected. Finally, 100,000 images of various objects existing on pedestrians and roads, 200,000 images of police and traffic safety personnel, 5,000 images of police and traffic safety personnel, and data sets consisting of 5,000 image data were collected and built.