面向自动驾驶系统的可扩展分布式基于仿真的测试
Scalable Distributed Simulation-Based Testing for Automated Driving Systems
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中文总结 AI 辅助
本文提出一种DevOps驱动的端到端框架，在轻量级Kubernetes集群上自动化CARLA场景测试的构建与分布式执行，200个场景测试的端到端时间较基线提速超8倍，为ADS可扩展仿真测试提供支撑。
中文摘要 AI 辅助
基于虚拟场景的测试是验证自动驾驶系统（ADS）和智能交通系统（ITS）的关键支撑技术。然而，执行包含可能数千个场景的大规模测试套件仍然劳动密集且难以扩展。本文提出了一种端到端、DevOps驱动的框架，该框架在轻量级Kubernetes集群上自动化ADS的基于CARLA的场景测试的构建、部署和分布式执行。ROS 2应用程序被打包为标准化的Kubernetes Helm图表，这些图表由仓库规范生成，而整个仿真环境则通过动态Helmfile清单以声明方式组成。本文描述了如何在Argo Workflows中实现分布式测试工作流，以配置环境、聚合和批量处理来自可配置源的OpenSCENARIO测试用例、跨集群节点并行执行场景，并收集日志和资源指标。在运行200个场景的多节点K3s集群上进行的评估中，最佳配置与顺序基线相比，将端到端工作流时间加快了8倍以上。结果表明，端到端执行时间有显著提升，并量化了并行性、编排开销和集群稳定性之间的权衡。该框架还在一个与场景源和下游评估模块连接的实际ADS测试应用中得到了验证，这表明该方法不仅为可扩展的仿真测试提供了坚实基础，还能生成可追溯的证据，为安全论证提供支持。
英文摘要
Virtual scenario-based testing is a key enabler for validating automated driving systems (ADS) and intelligent transport systems (ITS). However, executing large-scale test suites involving possibly thousands of scenarios remains labor-intensive and difficult to scale. This paper presents an end-to-end, DevOps-driven framework that automates build, deployment, and distributed execution of CARLA-based scenario tests of an ADS on a lightweight Kubernetes cluster. ROS 2 applications are packaged as standardized Kubernetes Helm charts generated from repository specifications, while entire simulation environments are composed declaratively via dynamic Helmfile manifests. The paper describes how a distributed testing workflow can be implemented in Argo Workflows to provision environments, aggregate and batch OpenSCENARIO test cases from configurable sources, execute scenarios in parallel across cluster nodes, and collect logs and resource metrics. In an evaluation on a multi-node K3s cluster running 200 scenarios, the best configuration speeds up end-to-end workflow time by more than a factor of eight compared to a sequential baseline. The results demonstrate significant gains in end-to-end execution time and quantify trade-offs between parallelism, orchestration overhead, and cluster stability. The framework is further demonstrated in a real-world ADS test application with connections to scenario sources and downstream evaluation modules. This demonstrates that the approach provides a strong foundation not only for scalable simulation testing, but also for generating traceable evidence that can support safety arguments.
发表机构
- RWTH Aachen University（亚琛工业大学）
- Institute for Automotive Engineering (ika)（汽车工程研究所（ika））
机构由 AI 辅助整理，请以论文原文为准。
