Behavior-Semantic Scenery Description (BSSD) of Road Networks for Automated Driving

TL;DR

This paper introduces a Behavior-Semantic Scenery Description (BSSD) to explicitly link scenery elements with the behavioral demands of Highly Automated Vehicles, enhancing safety validation and operational clarity.

Contribution

It proposes a novel BSSD framework that explicitly connects scenery to behavioral requirements, filling a gap in current scenery descriptions for automated driving.

Findings

BSSD effectively represents the behavior space of road scenery.

Integration of BSSD into Lanelet2 demonstrates practical applicability.

Supports development, testing, and operation of HAVs by clarifying behavioral boundaries.

Abstract

The safety approval of Highly Automated Vehicles (HAV) is economically infeasible with current approaches. For verification and validation, it is essential to describe the intended behavior of an HAV in the development process in order to prove safety. The demand for this behavior comes from the traffic rules which are instantiated by the present scenery around the vehicle (e.g. traffic signs or road markings). The Operational Design Domain (ODD) specifies the scenery in which an HAV may operate, but current descriptions fail to explicitly represent the associated behavioral demand of the scenery. We propose a new approach for a Behavior-Semantic Scenery Description (BSSD) in order to describe the behavior space of a present scenery. A behavior space represents the delimitation of the legally possible behavior. The BSSD explicitly links the scenery with the behavioral demand for HAV. Based on identified goals and challenges for such an approach, we derive requirements for a generic structure of the description for complete road networks. All required elements to represent the behavior space of the scenery are identified. Within real world examples, we present an instance of the BSSD integrated into the HD-map framework Lanelet2 to prove the applicability of the description. The presented approach supports development, test and operation of HAV by closing the knowledge gap of where a vehicle has to behave in which limits within an ODD.