Towards Establishing Systematic Classification Requirements for Automated Driving
This addresses the problem of aligning perception systems with legal standards for automated driving, but it is incremental as it builds on existing classification tasks.
The paper tackles the lack of consistent classification requirements in automated driving perception by proposing a structured method to generate a classification hierarchy based on legal and safety aspects, finding limited agreement with existing benchmark datasets.
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.