汽车感知软件开发：对数据、注释和生态系统挑战的实证研究
Automotive Perception Software Development: An Empirical Investigation into Data, Annotation, and Ecosystem Challenges
摘要：
包含机器学习算法的软件是汽车感知的组成部分，例如，在驾驶自动化系统中。这种软件的开发，特别是机器学习组件的训练和验证，需要大型注释数据集。数据和注释服务行业已经出现，以服务于这种数据密集型汽车软件组件的开发。指定数据和注释需求的广泛困难挑战了OEM（原始设备制造商）及其软件组件，数据和注释供应商之间的合作。本文探讨了这些困难的原因，在瑞典汽车行业的从业人员，以达到明确的规格数据和注释。访谈研究的结果表明，缺乏有效的数据质量方面的指标，工作方式的模糊性，注释质量的定义不明确，以及商业生态系统中的缺陷是导致难以获得规范的原因。我们提供了一个建议列表，可以减轻在获得规格时的挑战，我们提出了未来的研究机会，以克服这些挑战。我们的工作有助于对应用于复杂软件系统的机器学习的问责制进行持续研究，特别是对于自动驾驶等高风险应用。
英文摘要：
Software that contains machine learning algorithms is an integral part of automotive perception, for example, in driving automation systems. The development of such software, specifically the training and validation of the machine learning components, require large annotated datasets. An industry of data and annotation services has emerged to serve the development of such data-intensive automotive software components. Wide-spread difficulties to specify data and annotation needs challenge collaborations between OEMs (Original Equipment Manufacturers) and their suppliers of software components, data, and annotations. This paper investigates the reasons for these difficulties for practitioners in the Swedish automotive industry to arrive at clear specifications for data and annotations. The results from an interview study show that a lack of effective metrics for data quality aspects, ambiguities in the way of working, unclear definitions of annotation quality, and deficits in the business ecosystems are causes for the difficulty in deriving the specifications. We provide a list of recommendations that can mitigate challenges when deriving specifications and we propose future research opportunities to overcome these challenges. Our work contributes towards the on-going research on accountability of machine learning as applied to complex software systems, especially for high-stake applications such as automated driving.
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。
