Contemporary connected vehicles host numerous applications, such as diagnostics and navigation, and new software is continuously being developed. However, the development process typically requires offline batch processing of large data volumes. In an edge computing approach, data analysts and developers can instead process sensor data directly on computational resources inside vehicles. This enables rapid prototyping to shorten development cycles and reduce the time to create new business values or insights. This paper presents the design, implementation, and operation of the AutoSPADA edge computing platform for distributed data analytics. The platform's design follows scalability, reliability, resource efficiency, privacy, and security principles promoted through mature and industrially proven technologies. In AutoSPADA, computational tasks are general Python scripts, and we provide a library to, for example, read signals from the vehicle and publish results to the cloud. Hence, users only need Python knowledge to use the platform. Moreover, the platform is designed to be extended to support additional programming languages.

翻译：现代网联车辆承载着诊断、导航等众多应用，且新型软件持续开发中。然而，其开发流程通常需要对海量数据进行离线批处理。采用边缘计算方式后，数据分析师与开发者可直接在车端计算资源上处理传感器数据，从而实现快速原型开发，缩短开发周期，加速新商业价值或洞察的创造。本文介绍了面向分布式数据分析的AutoSPADA边缘计算平台的设计、实现与运行。该平台遵循可扩展性、可靠性、资源效率、隐私及安全性原则，这些原则通过成熟且经工业验证的技术得以强化。在AutoSPADA中，计算任务采用通用Python脚本实现，我们提供了用于从车辆读取信号并发布结果至云端的工具库。因此，用户仅需掌握Python知识即可使用该平台。此外，平台设计具备扩展性，可支持更多编程语言。