🤖 AI Summary
AI-based trajectory planners for autonomous driving exhibit high operational risk and poor interpretability in unknown or under-trained scenarios. Method: This paper proposes a knowledge graph–based framework for explainable capability assessment. It models driving data as a structured knowledge graph to explicitly represent scene semantics in human-understandable form, and dynamically evaluates model proficiency across sub-scenarios via subgraph querying, scene complexity quantification, and dataset coverage analysis. Contribution/Results: Experiments on the NuPlan dataset demonstrate that the method effectively identifies high-risk driving situations where the planner is insufficiently trained, significantly enhancing the interpretability of evaluation outcomes and deployment trustworthiness. By grounding assessment in semantically meaningful, queryable structures, the approach establishes a novel paradigm for robustness verification of safety-critical AI systems.
📝 Abstract
Automated driving functions increasingly rely on machine learning for tasks like perception and trajectory planning, requiring large, relevant datasets. The performance of these algorithms depends on how closely the training data matches the task. To ensure reliable functioning, it is crucial to know what is included in the dataset to assess the trained model's operational risk. We aim to enhance the safe use of machine learning in automated driving by developing a method to recognize situations that an automated vehicle has not been sufficiently trained on. This method also improves explainability by describing the dataset at a human-understandable level. We propose modeling driving data as knowledge graphs, representing driving scenes with entities and their relationships. These graphs are queried for specific sub-scene configurations to check their occurrence in the dataset. We estimate a vehicle's competence in a driving scene by considering the coverage and complexity of sub-scene configurations in the training set. Higher complexity scenes require greater coverage for high competence. We apply this method to the NuPlan dataset, modeling it with knowledge graphs and analyzing the coverage of specific driving scenes. This approach helps monitor the competence of machine learning models trained on the dataset, which is essential for trustworthy AI to be deployed in automated driving.