# Challenges in Designing Datasets and Validation for Autonomous Driving

## TL;DR

This paper discusses the challenges and common pitfalls in designing datasets and validation methods for autonomous driving, emphasizing the gap between academic research and industrial deployment.

## Contribution

It highlights the often overlooked issues in dataset design and validation for autonomous driving, advocating for better formalization and industrial relevance.

## Key findings

- Identifies common problems and wrong assumptions in dataset design.
- Highlights the gap between academic datasets and industrial needs.
- Proposes steps to improve dataset validation and design.

## Abstract

Autonomous driving is getting a lot of attention in the last decade and will be the hot topic at least until the first successful certification of a car with Level 5 autonomy. There are many public datasets in the academic community. However, they are far away from what a robust industrial production system needs. There is a large gap between academic and industrial setting and a substantial way from a research prototype, built on public datasets, to a deployable solution which is a challenging task. In this paper, we focus on bad practices that often happen in the autonomous driving from an industrial deployment perspective. Data design deserves at least the same amount of attention as the model design. There is very little attention paid to these issues in the scientific community, and we hope this paper encourages better formalization of dataset design. More specifically, we focus on the datasets design and validation scheme for autonomous driving, where we would like to highlight the common problems, wrong assumptions, and steps towards avoiding them, as well as some open problems.