Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE

Abstract

Approval of ADS depends on evaluating its behavior within representative real-world traffic scenarios. A common way to obtain such scenarios is to extract them from real-world data recordings. These can then be grouped and serve as basis on which the ADS is subsequently tested. This poses two central challenges: how scenarios are extracted and how they are grouped. Existing extraction methods rely on heterogeneous definitions, hindering scenario comparability. For the grouping of scenarios, rule-based or ML-based methods can be utilized. However, while modern ML-based approaches can handle the complexity of traffic scenarios, unlike rule-based approaches, they lack interpretability and may not align with domain-knowledge. This work contributes to a standardized scenario extraction based on the Scenario-as-Specification concept, as well as a domain-knowledge-guided scenario clustering process. Experiments on the highD dataset demonstrate that scenarios can be extracted reliably and that domain-knowledge can be effectively integrated into the clustering process. As a result, the proposed methodology supports a more standardized process for deriving scenario categories from highway data recordings and thus enables a more efficient validation process of automated vehicles.

Figures (3)

• Figure 1: Overview of the proposed method for knowledge guided scenario extraction and clustering.
• Figure 2: In the Scenario Preprocessing stage, ego–behavior changes are detected and used to extract scenarios from the traffic dataset. In addition, the interaction matrix $\bm{T}^{(m)}$ and the pseudo-class label vector $\bm{s}^{(m)}$ are computed for each scenario. For clustering, a CVQ-VAE with a predefined number of codebook entries $Q$ is employed. The model receives only the scenario trajectories $\bm{\xi}^{(m)}$ as input, produces a discretized representation $\bm{z_q}^{(m)}$, and predicts both the interaction matrix and the behavior class from this latent representation.
• Figure 3: Exemplary augmented scenario where the additional vehicle (green) has no influence on the ego vehicle (red).