Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle

TL;DR

This paper introduces a rule-based classification method using the Prediction Divergence Principle to improve AEBS validation by better distinguishing true and false activations, especially in complex real-world testing scenarios.

Contribution

It presents a novel objective intervention classification approach that reduces reliance on subjective human labeling in AEBS validation.

Findings

The method improves classification transparency and consistency.

It identifies key strengths and limitations of the approach.

Future refinements are outlined for broader application.

Abstract

The safety validation of automatic emergency braking system (AEBS) requires accurately distinguishing between false positive (FP) and true positive (TP) system activations. While simulations allow straightforward differentiation by comparing scenarios with and without interventions, analyzing activations from open-loop resimulations - such as those from field operational testing (FOT) - is more complex. This complexity arises from scenario parameter uncertainty and the influence of driver interventions in the recorded data. Human labeling is frequently used to address these challenges, relying on subjective assessments of intervention necessity or situational criticality, potentially introducing biases and limitations. This work proposes a rule-based classification approach leveraging the Prediction Divergence Principle (PDP) to address those issues. Applied to a simplified AEBS, the proposed method reveals key strengths, limitations, and system requirements for effective implementation. The findings suggest that combining this approach with human labeling may enhance the transparency and consistency of classification, thereby improving the overall validation process. While the rule set for classification derived in this work adopts a conservative approach, the paper outlines future directions for refinement and broader applicability. Finally, this work highlights the potential of such methods to complement existing practices, paving the way for more reliable and reproducible AEBS validation frameworks.