In the automotive industry, the rise of software-defined vehicles (SDVs) has driven a shift toward virtualization-based architectures that consolidate diverse automotive workloads on a shared hardware platform. To support this evolution, chipset vendors provide board support packages (BSPs), hypervisor setups, and resource allocation guidelines. However, adapting these static configurations to varying system requirements and workloads remain a significant challenge for Tier 1 integrators. This paper presents an automated scenario generation framework, which helps automotive vendors to allocate hardware resources efficiently across multiple VMs. By profiling runtime behavior and integrating both theoretical models and vendor heuristics, the proposed tool generates optimized hypervisor configurations tailored to system constraints. We compare two main approaches for modeling target QoS based on profiled data and resource allocation: domain-guided parametric modeling and deep learning-based modeling. We further describe our optimization strategy using the selected QoS model to derive efficient resource allocations. Finally, we report on real-world deployments to demonstrate the effectiveness of our framework in improving integration efficiency and reducing development time in resource-constrained environments.

翻译：在汽车行业，软件定义汽车（SDV）的兴起推动了向基于虚拟化的架构转变，该架构将多样化的汽车工作负载整合到共享硬件平台上。为支持这一演进，芯片供应商提供了板级支持包（BSP）、Hypervisor设置以及资源分配指南。然而，对于一级集成商而言，将这些静态配置适配于变化的系统需求和工作负载仍然是一项重大挑战。本文提出了一种自动化场景生成框架，旨在帮助汽车供应商在多个虚拟机（VM）间高效分配硬件资源。通过分析运行时行为，并整合理论模型与供应商启发式规则，所提出的工具能够生成针对系统约束优化的Hypervisor配置。我们比较了两种基于分析数据与资源分配来建模目标服务质量（QoS）的主要方法：领域引导的参数化建模与基于深度学习的建模。我们进一步描述了使用选定QoS模型来推导高效资源分配的优化策略。最后，我们报告了实际部署案例，以证明本框架在资源受限环境中提升集成效率与缩短开发时间方面的有效性。