将ROS推向阴暗面:用于自动驾驶汽车混合现实测试系统的基于ROS的联合仿真架构

M. Zofka, Lars Töttel, Maximilian Zipfl, Marc Heinrich, Tobias Fleck, P. Schulz, Johann Marius Zöllner
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引用次数: 6

摘要

自动驾驶汽车的验证和验证仍然是一个未解决的问题。尽管虚拟方法有望提供一种经济高效且可复制的解决方案,但为了对高度自动驾驶(HAD)功能的性能做出有价值的陈述,需要对现实世界交通领域进行最全面、最真实的描述。来自不同领域专家的模型提供了此类表示的存储库。然而,这些模型必须连接在一起,以便对真实世界的交通域进行广泛和统一的映射,以进行HAD性能评估。因此,我们提出了基于机器人操作系统(ROS)的联合仿真架构的概念,用于将不同领域的专家模型、真实行人的沉浸和刺激以及AD系统耦合和集成到一个共同的测试系统中。这为多感官AD系统的性能评估提供了一种统一的方法来生成地面真实值。我们通过在混合现实环境中耦合行为模型来演示ROS驱动的联合仿真的适用性。
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Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles
Validation and verification of autonomous vehicles is still an unsolved problem. Although virtual approaches promise a cost efficient and reproducible solution, a most comprehensive and realistic representation of the real world traffic domain is required in order to make valuable statements about the performance of a highly automated driving (HAD) function. Models from different domain experts offer a repository of such representations. However, these models must be linked together for an extensive and uniform mapping of real world traffic domain for HAD performance assessment.Hereby, we propose the concept of a co-simulation architecture built upon the Robot Operating System (ROS) for both coupling and for integration of different domain expert models, immersion and stimulation of real pedestrians as well as AD systems into a common test system. This enables a unified way of generating ground truth for the performance assessment of multi-sensorial AD systems. We demonstrate the applicability of the ROS powered co-simulation by coupling behavior models in our mixed reality environment.
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