Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

TL;DR

This paper analyzes over 200 autonomous driving datasets to understand their impact and influence, introducing an early assessment score to evaluate datasets without relying solely on citation counts.

Contribution

It provides a comprehensive scientometric analysis of AD datasets and proposes an Influence Score for early impact assessment.

Findings

Identified factors correlating with dataset citations

Developed an Influence Score for early impact prediction

Analyzed relationships between dataset metadata and influence

Abstract

Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.