The integration of service-oriented architectures (SOA) with function offloading for distributed, intelligent transportation systems (ITS) offers the opportunity for connected autonomous vehicles (CAVs) to extend their locally available services. One major goal of offloading a subset of functions in the processing chain of a CAV to remote devices is to reduce the overall computational complexity on the CAV. The extension of using remote services, however, requires careful safety analysis, since the remotely created data are corrupted more easily, e.g., through an attacker on the remote device or by intercepting the wireless transmission. To tackle this problem, we first analyze the concept of SOA for distributed environments. From this, we derive a safety framework that validates the reliability of remote services and the data received locally. Since it is possible for the autonomous driving task to offload multiple different services, we propose a specific multi-staged framework for safety analysis dependent on the service composition of local and remote services. For efficiency reasons, we directly include the multi-staged framework for safety analysis in our service-oriented function offloading framework (SOFOF) that we have proposed in earlier work. The evaluation compares the performance of the extended framework considering computational complexity, with energy savings being a major motivation for function offloading, and its capability to detect data from corrupted remote services.

翻译：面向服务体系结构（SOA）与分布式智能交通系统（ITS）中功能卸载技术的融合，为联网自动驾驶车辆（CAV）扩展其本地可用服务提供了可能。将CAV处理链中的部分功能卸载至远程设备的主要目标之一是降低CAV的整体计算复杂度。然而，远程服务的使用扩展需要谨慎的安全性分析，因为远程生成的数据更易受到破坏（例如通过远程设备上的攻击者或无线传输拦截）。为解决此问题，我们首先分析了分布式环境下的SOA概念。基于此，我们推导出一个用于验证远程服务可靠性及本地接收数据安全性的分析框架。鉴于自动驾驶任务可能卸载多个不同服务，我们提出一种基于本地与远程服务组合的多阶段安全性分析框架。出于效率考量，我们将该多阶段安全性分析框架直接集成到我们早期工作中提出的面向服务功能卸载框架（SOFOF）中。评估工作从计算复杂度角度比较了扩展框架的性能（节能是功能卸载的主要动机），并检验了其检测来自受损远程服务数据的能力。