基于搜索的车内场景理解视觉语言模型测试
Search-based Testing of Vision Language Models for In-Car Scene Understanding
AI总结：
提出ISU-Test方法，结合渲染场景生成与搜索测试，通过优化场景参数自动生成多样化车内场景，评估VLM在问答和字幕任务中的性能，相比随机生成故障率提高10倍，故障覆盖率提高3.6倍。
摘要：
在汽车领域，车内场景理解（ISU）能够检测安全关键事件（如驾驶员分心），并通过分析车内场景和调整环境（如环境照明）来支持驾驶员或乘客。工业界正越来越多地探索视觉语言模型（VLM）来解释摄像头记录的车内场景，并为下游推理任务提取信息。然而，VLM可能生成不完整、错误或误导性的场景描述，凸显了系统测试的必要性。收集真实车内数据成本高昂、难以扩展，且在早期设计阶段通常不可行。本文提出ISU-Test，一种自动化测试方法，结合基于渲染的场景生成与基于搜索的测试来评估ISU系统。通过将测试构建为优化问题并系统修改场景参数，我们的方法生成多样化的车内场景并探索广泛的配置。我们在工业原型和开源VLM上，通过两个案例研究（问答和字幕）评估ISU-Test，并与随机场景生成进行对比。结果表明，ISU-Test显著优于基线，故障率提高高达10倍，故障覆盖率提高高达3.6倍。
英文摘要：
In the automotive domain, in-car scene understanding (ISU) enables the detection of safety-critical events, such as driver distraction, and supports drivers or passengers by analyzing the in-car scene and adapting the environment (e.g., ambient lighting). The industry is increasingly exploring vision-language models (VLMs) to interpret camera-recorded in-car scenes and extract information for downstream reasoning tasks. However, VLMs may generate incomplete, erroneous, or misleading scene descriptions, highlighting the need for systematic testing. Collecting real in-vehicle data is costly, difficult to scale, and often infeasible, particularly in early design stages. In this paper, we present ISU-Test, an automated testing approach that combines rendering-based scene generation with search-based testing to evaluate ISU systems. By framing testing as an optimization problem and systematically modifying scene parameters, our method generates diverse in-car scenarios and explores a wide range of configurations. We evaluate ISU-Test on both an industrial prototype and open-source VLMs across two case studies: question answering and captioning, comparing against randomized scenario generation. Results show that ISU-Test significantly outperforms the baseline, achieving up to 10 times higher failure rates and up to 3.6 times higher failure coverage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
