Vision-Language-Action (VLA)-based driving systems represent a significant paradigm shift in autonomous driving since, by combining traffic scene understanding, linguistic interpretation, and action generation, these systems enable more flexible, adaptive, and instruction-responsive driving behaviors. However, despite their growing adoption and potential to support socially responsible autonomous driving as well as understanding high-level human instructions, VLA-based driving systems may exhibit new types of hazardous behaviors. For instance, the integration of open-ended natural language inputs (e.g., user or navigation instructions) into the multimodal control loop may lead to unpredictable and unsafe behaviors that could endanger vehicle occupants and pedestrians. Hence, assuring the safety of these systems is crucial to help build trust in their operations. To support this, we propose a novel safety case design approach called RAISE. Our approach introduces novel patterns tailored to instruction-based driving systems such as VLA-based driving systems, an extension of Hazard Analysis and Risk Assessment (HARA) detailing safe scenarios and their outcomes, and a design technique to create the safety cases of VLA-based driving systems. A case study on SimLingo illustrates how our approach can be used to construct rigorous, evidence-based safety claims for this emerging class of autonomous driving systems.

翻译：基于视觉-语言-动作（VLA）的驾驶系统代表了自动驾驶领域的重大范式转变，因为通过融合交通场景理解、语言解释和动作生成，这些系统能够实现更灵活、更具适应性且能响应指令的驾驶行为。然而，尽管它们被日益广泛采用，并具备支持社会责任型自动驾驶以及理解高级人类指令的潜力，基于VLA的驾驶系统仍可能展现出新型危险行为。例如，将开放式自然语言输入（如用户或导航指令）集成到多模态控制回路中，可能导致不可预测且不安全的后果，危及车辆乘员和行人。因此，确保这些系统的安全性对于建立对其运行的信任至关重要。为此，我们提出了一种名为RAISE的新型安全案例设计方法。我们的方法引入了针对基于指令的驾驶系统（如基于VLA的驾驶系统）的特有模式，对危害分析与风险评估（HARA）进行了扩展，详细描述了安全场景及其结果，并提供了一种为基于VLA的驾驶系统构建安全案例的设计技术。以SimLingo为例的案例研究表明，我们的方法可用于为这类新兴的自动驾驶系统构建严谨、基于证据的安全声明。