# VistaScenario: Interaction Scenario Engineering for Vehicles with Intelligent Systems for Transport Automation

arXiv: https://arxiv.org/abs/2402.07720 · HTML: https://mlsanity.com/paper/2402.07720/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications](https://mlsanity.com/paper/2504.04419/) (2025)
- [VLADriver-RAG: Retrieval-Augmented Vision-Language-Action Models for Autonomous Driving](https://mlsanity.com/paper/2605.08133/) (2026)
- [Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph](https://mlsanity.com/paper/2507.05086/) (2025)
- [Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE](https://mlsanity.com/paper/2603.16964/) (2026)

## Abstract (arXiv)

Intelligent vehicles and autonomous driving systems rely on scenario engineering for intelligence and index (I&I), calibration and certification (C&C), and verification and validation (V&V). To extract and index scenarios, various vehicle interactions are worthy of much attention, and deserve refined descriptions and labels. However, existing methods cannot cope well with the problem of scenario classification and labeling with vehicle interactions as the core. In this paper, we propose VistaScenario framework to conduct interaction scenario engineering for vehicles with intelligent systems for transport automation. Based on the summarized basic types of vehicle interactions, we slice scenario data stream into a series of segments via spatiotemporal scenario evolution tree. We also propose the scenario metric Graph-DTW based on Graph Computation Tree and Dynamic Time Warping to conduct refined scenario comparison and labeling. The extreme interaction scenarios and corner cases can be efficiently filtered and extracted. Moreover, with naturalistic scenario datasets, testing examples on trajectory prediction model demonstrate the effectiveness and advantages of our framework. VistaScenario can provide solid support for the usage and indexing of scenario data, further promote the development of intelligent vehicles and transport automation.