Data is both the key enabler and a major bottleneck for machine learning in autonomous driving. Effective model training requires not only large quantities of sensor data but also balanced coverage that includes rare yet safety-critical scenarios. Capturing such events demands extensive driving time and efficient selection. This paper introduces the Lambda framework, an edge-native platform that enables on-vehicle data filtering and processing through user-defined functions. The framework provides a serverless-inspired abstraction layer that separates application logic from low-level execution concerns such as scheduling, deployment, and isolation. By adapting Function-as-a-Service (FaaS) principles to resource-constrained automotive environments, it allows developers to implement modular, event-driven filtering algorithms while maintaining compatibility with ROS 2 and existing data recording pipelines. We evaluate the framework on an NVIDIA Jetson Orin Nano and compare it against native ROS 2 deployments. Results show competitive performance, reduced latency and jitter, and confirm that lambda-based abstractions can support real-time data processing in embedded autonomous driving systems. The source code is available at https://github.com/LASFAS/jblambda.

翻译：数据既是自动驾驶中机器学习的关键赋能因素，也是一个主要瓶颈。有效的模型训练不仅需要大量的传感器数据，还需要包含罕见但安全关键场景的均衡覆盖。捕获此类事件需要大量的驾驶时间和高效的选择。本文介绍了Lambda框架，这是一个边缘原生平台，通过用户定义函数实现车载数据过滤与处理。该框架提供了一个受无服务器启发的抽象层，将应用逻辑与调度、部署和隔离等底层执行关注点分离开来。通过将函数即服务（FaaS）原则适配到资源受限的汽车环境中，它使开发人员能够实现模块化、事件驱动的过滤算法，同时保持与ROS 2及现有数据记录管道的兼容性。我们在NVIDIA Jetson Orin Nano上评估了该框架，并将其与原生ROS 2部署进行了比较。结果表明其具有有竞争力的性能、更低的延迟和抖动，并证实了基于lambda的抽象能够支持嵌入式自动驾驶系统中的实时数据处理。源代码可在 https://github.com/LASFAS/jblambda 获取。