Incremental Validation of Automated Driving Functions using Generic Volumes in Micro- Operational Design Domains

TL;DR

This paper presents a structured approach to validate automated driving functions by subdividing operational domains into micro-ODDs, enabling systematic testing of perception systems using generic obstacle representations in a virtual environment.

Contribution

It introduces the concept of micro-ODDs and a method for deriving test cases with abstract object representations for automated driving validation.

Findings

Systematic exploration of obstacle detection edge cases.

Perception quality evaluation based on vehicle behaviour outcomes.

Demonstration of the approach in a virtual, photorealistic simulation environment.

Abstract

The validation of highly automated, perception-based driving systems must ensure that they function correctly under the full range of real-world conditions. Scenario-based testing is a prominent approach to addressing this challenge, as it involves the systematic simulation of objects and environments. Operational Design Domains (ODDs) are usually described using a taxonomy of qualitative designations for individual objects. However, the process of transitioning from taxonomy to concrete test cases remains unstructured, and completeness is theoretical. This paper introduces a structured method of subdividing the ODD into manageable sections, termed micro-ODDs (mODDs), and deriving test cases with abstract object representations. This concept is demonstrated using a one-dimensional, laterally guided manoeuvre involving a shunting locomotive within a constrained ODD. In this example, mODDs are defined and refined into narrow taxonomies that enable test case generation. Obstacles are represented as generic cubes of varying sizes, providing a simplified yet robust means of evaluating perception performance. A series of tests were conducted in a closed-loop, co-simulated virtual environment featuring photorealistic rendering and simulated LiDAR, GNSS and camera sensors. The results demonstrate how edge cases in obstacle detection can be systematically explored and how perception quality can be evaluated based on observed vehicle behaviour, using crash versus safe stop as the outcome metrics. These findings support the development of a standardised framework for safety argumentation and offer a practical step towards the validation and authorisation of automated driving functions.