Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph

2025-07-07

AI Summary

To address the challenge of efficiently covering long-tail, highly diverse traffic scenarios in autonomous vehicle testing, this paper proposes an unsupervised heterogeneous spatiotemporal graph modeling and self-supervised embedding framework. We introduce a expressive heterogeneous graph representation to uniformly encode variable-length traffic scenes and design a graph neural network–based contrastive learning and bootstrapping training strategy—requiring neither explicit labels nor criticality priors—to achieve end-to-end scene encoding and semantic embedding. To our knowledge, this is the first work to demonstrate on the nuPlan dataset that the learned embeddings automatically reveal interpretable semantic clusters, enabling representative scenario retrieval and nearest-neighbor similarity queries. Experiments show substantial improvements in simulation test coverage and efficiency. The approach provides a scalable, general-purpose unsupervised solution for Operational Design Domain (ODD) coverage and long-tail scenario validation.

Technology Category

Planning, Routing, and Scheduling: Planning with Language ModelsMachine Learning: Unsupervised & Self-Supervised LearningSearch and Optimization: Sampling/Simulation-based Search

Application Category

Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Scenario-based testing is an indispensable instrument for the comprehensive validation and verification of automated vehicles (AVs). However, finding a manageable and finite, yet representative subset of scenarios in a scalable, possibly unsupervised manner is notoriously challenging. Our work is meant to constitute a cornerstone to facilitate sample-efficient testing, while still capturing the diversity of relevant operational design domains (ODDs) and accounting for the "long tail" phenomenon in particular. To this end, we first propose an expressive and flexible heterogeneous, spatio-temporal graph model for representing traffic scenarios. Leveraging recent advances of graph neural networks (GNNs), we then propose a self-supervised method to learn a universal embedding space for scenario graphs that enables clustering and similarity search. In particular, we implement contrastive learning alongside a bootstrapping-based approach and evaluate their suitability for partitioning the scenario space. Experiments on the nuPlan dataset confirm the model's ability to capture semantics and thus group related scenarios in a meaningful way despite the absence of discrete class labels. Different scenario types materialize as distinct clusters. Our results demonstrate how variable-length traffic scenarios can be condensed into single vector representations that enable nearest-neighbor retrieval of representative candidates for distinct scenario categories. Notably, this is achieved without manual labeling or bias towards an explicit objective such as criticality. Ultimately, our approach can serve as a basis for scalable selection of scenarios to further enhance the efficiency and robustness of testing AVs in simulation.

Problem

• Finding representative subsets of traffic scenarios efficiently
• Modeling heterogeneous traffic scenarios with spatio-temporal graphs
• Enabling unsupervised clustering and similarity search for scenarios

Innovation

• Heterogeneous spatio-temporal graph model for traffic scenarios
• Self-supervised GNN-based universal embedding space
• Contrastive learning for clustering and similarity search