Perspective, Survey and Trends: Public Driving Datasets and Toolsets for Autonomous Driving Virtual Test

TL;DR

This paper systematically reviews publicly available autonomous driving datasets and toolsets from 2000 to 2020, providing insights and trends to aid system design and future research in virtual testing.

Contribution

It is the first empirical survey combining datasets and toolsets for autonomous driving testing using a systematic literature review approach.

Findings

Analysis of 70 datasets and 35 toolsets reveals key scenario coverage.

Identifies trends and gaps in autonomous driving virtual testing resources.

Provides recommendations for future dataset and toolset development.

Abstract

Owing to the merits of early safety and reliability guarantee, autonomous driving virtual testing has recently gains increasing attention compared with closed-loop testing in real scenarios. Although the availability and quality of autonomous driving datasets and toolsets are the premise to diagnose the autonomous driving system bottlenecks and improve the system performance, due to the diversity and privacy of the datasets and toolsets, collecting and featuring the perspective and quality of them become not only time-consuming but also increasingly challenging. This paper first proposes a Systematic Literature review approach for Autonomous driving tests (SLA), then presents an overview of existing publicly available datasets and toolsets from 2000 to 2020. Quantitative findings with the scenarios concerned, perspectives and trend inferences and suggestions with 35 automated driving test tool sets and 70 test data sets are also presented. To the best of our knowledge, we are the first to perform such recent empirical survey on both the datasets and toolsets using a SLA based survey approach. Our multifaceted analyses and new findings not only reveal insights that we believe are useful for system designers, practitioners and users, but also can promote more researches on a systematic survey analysis in autonomous driving surveys on dataset and toolsets.