Towards Establishing Systematic Classification Requirements for Automated Driving

TL;DR

This paper proposes a structured method to define classification requirements for automated driving, integrating legal and safety considerations to improve perception benchmarks.

Contribution

It introduces a systematic approach to generate classification structures based on legal and safety requirements, addressing inconsistencies in existing benchmarks.

Findings

Limited agreement between proposed structure and existing datasets

Legal requirements significantly influence perception classification needs

Highlights the need for explicit legal considerations in perception benchmarks

Abstract

Despite the presence of the classification task in many different benchmark datasets for perception in the automotive domain, few efforts have been undertaken to define consistent classification requirements. This work addresses the topic by proposing a structured method to generate a classification structure. First, legal categories are identified based on behavioral requirements for the vehicle. This structure is further substantiated by considering the two aspects of collision safety for objects as well as perceptual categories. A classification hierarchy is obtained by applying the method to an exemplary legal text. A comparison of the results with benchmark dataset categories shows limited agreement. This indicates the necessity for explicit consideration of legal requirements regarding perception.