Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark

2026-07-01

AI Summary

This work addresses the lack of systematic approaches for resource-constrained teams to efficiently construct high-impact autonomous driving datasets. It proposes a strategic framework initiated by diagnosing research gaps to determine whether performance bottlenecks stem from data or evaluation limitations. Based on this diagnosis, the framework selects minimal-cost data operators and jointly optimizes sensor configuration and annotation strategies. Introducing the novel “build-on-demand” paradigm, the method achieves maximal scientific utility through minimally necessary data operations. The resulting methodology has been successfully applied to the KITScenes dataset series, substantially improving both data construction efficiency and research relevance, thereby offering a reusable design guideline for autonomous driving datasets tailored to small- to medium-sized research teams.

Abstract

Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategically design impactful ones. This is especially limiting for small and medium-sized labs and startups that cannot afford to misallocate scarce resources. We argue that impactful dataset creation begins with a diagnosis: whether a research question is blocked by a data problem or an evaluation problem, and proceeds by selecting the minimal data operator(s) that closes the resulting gap, recording new data only when no cheaper operator(s) suffices. We analyze the evolution of major autonomous driving (AD) datasets through this lens and distill a strategic framework spanning gap identification, operator choice, sensor suite design, and annotation strategy. We ground the framework in a running case study of our KITScenes dataset family. The datasets are available at: https://kitscenes.com/

Problem

• autonomous driving datasets
• research gap
• strategic design
• impactful dataset creation
• resource allocation

Innovation

• strategic dataset design
• data operators
• research gap diagnosis
• autonomous driving datasets
• minimal data collection