基于邻域覆盖与相似性的自动驾驶汽车少样本场景测试
Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity
AI总结：
本文首次提出自动驾驶汽车少样本测试问题，通过基于邻域覆盖与相似性的优化框架动态选择测试场景，并利用代理模型先验信息建立评估误差上界，在有限测试数量下显著降低评估误差与方差。
摘要：
在大规模部署之前，测试和评估自动驾驶汽车（AVs）的安全性能至关重要。实际上，由于测试预算和时间的严格限制，特定自动驾驶汽车可允许的测试场景数量受到严重制约。在测试数量严格受限的情况下，现有测试方法往往会导致显著的不确定性或难以量化评估结果。本文首次将该问题形式化为“少样本测试”（FST）问题，并提出一个系统性框架来应对这一挑战。为了缓解小规模测试场景集合中固有的显著不确定性，我们将FST问题构建为一个优化问题，并基于邻域覆盖和相似性来搜索测试场景集合。具体而言，在测试场景集合对自动驾驶汽车具有更好泛化能力的指导下，我们基于覆盖率动态调整该集合以及每个测试场景对评估结果的贡献，并利用代理模型（SMs）的先验信息。在关于代理模型的特定假设下，建立了评估误差的理论上界，以验证在给定有限测试数量内评估精度的充分性。在切入场景上的实验结果表明，与传统测试方法相比，我们的方法显著降低了评估误差和方差，尤其是在场景数量受到严格限制的情况下。
英文摘要：
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.
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