# Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity

arXiv: https://arxiv.org/abs/2402.01795 · HTML: https://mlsanity.com/paper/2402.01795/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 2 Feb 2024.

## Builds on (most cited first, 12 of 12)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- ["Curse of rarity" for autonomous vehicles](https://mlsanity.com/paper/2207.02749/) (2022)
- [Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques](https://mlsanity.com/paper/1605.04965/) (2016)
- [Ontology based Scene Creation for the Development of Automated Vehicles](https://mlsanity.com/paper/1704.01006/) (2017)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology](https://mlsanity.com/paper/1905.03419/) (2019)
- [Waymo's Safety Methodologies and Safety Readiness Determinations](https://mlsanity.com/paper/2011.00054/) (2020)
- [An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization](https://mlsanity.com/paper/2004.02121/) (2020)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies](https://mlsanity.com/paper/1905.03428/) (2019)
- [Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification](https://mlsanity.com/paper/2004.02126/) (2020)
- [Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review](https://mlsanity.com/paper/2103.04364/) (2021)
- [Adaptive Safety Evaluation for Connected and Automated Vehicles with Sparse Control Variates](https://mlsanity.com/paper/2212.00517/) (2022)
- [Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios](https://mlsanity.com/paper/2207.09259/) (2022)

## Built on by (most cited first, 2 of 2)

- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)
- [Towards provable probabilistic safety for scalable embodied AI systems](https://mlsanity.com/paper/2506.05171/) (2025)

## Abstract (arXiv)

Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible for a specific AV is severely limited by tight constraints on testing budgets and time. With the restrictions imposed by strictly restricted numbers of tests, existing testing methods often lead to significant uncertainty or difficulty to quantifying evaluation results. In this paper, we formulate this problem for the first time the "few-shot testing" (FST) problem and propose a systematic framework to address this challenge. To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity. Specifically, under the guidance of better generalization ability of the testing scenario set on AVs, we dynamically adjust this set and the contribution of each testing scenario to the evaluation result based on coverage, leveraging the prior information of surrogate models (SMs). With certain hypotheses on SMs, a theoretical upper bound of evaluation error is established to verify the sufficiency of evaluation accuracy within the given limited number of tests. The experiment results on cut-in scenarios demonstrate a notable reduction in evaluation error and variance of our method compared to conventional testing methods, especially for situations with a strict limit on the number of scenarios.