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arXiv 2311.09784cs.LOcs.AIcs.SE
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自动驾驶系统系统级仿真验证场景的自动生成

Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems

Fondazione Bruno Kessler(布鲁诺·凯斯勒基金会);University of Trento(特伦托大学)

Srajan Goyal, Alberto Griggio, Jacob Kimblad, Stefano Tonetta

AI总结:

针对自动驾驶系统验证难题,本文提出基于VIVAS框架,集成CARLA与ScenarioRunner自动生成系统级仿真场景,通过覆盖率标准指导,有效测试AI组件,验证了方法的有效性。

摘要:

随着自动驾驶系统(ADS)复杂性的不断增加,确保其安全性和可靠性已成为一项关键挑战。当采用人工智能组件来实现感知和/或控制功能时,这些系统的验证与确认(V&V)尤其困难。在欧空局资助的VIVAS项目中,我们开发了一个用于自主系统系统级仿真V&V的通用框架。该方法基于系统的仿真模型、符号化描述系统行为的抽象模型,以及用于生成场景和验证仿真执行的正式方法。可以定义各种覆盖率标准来指导场景的自动生成。在本文中,我们描述了VIVAS框架在ADS案例研究中的实例化。这基于广泛使用的驾驶模拟器CARLA及其ScenarioRunner工具的集成,该工具能够创建多样且复杂的驾驶场景。这也被用于CARLA自动驾驶挑战赛,以验证CARLA社区共享的基于AI的不同ADS代理的感知和控制能力。我们描述了一个抽象ADS模型的开发,以及一个关注待验证ADS车辆相对行为的覆盖率标准的制定。利用VIVAS框架,我们生成并执行各种驾驶场景,从而测试AI组件的能力。结果表明,VIVAS在使用CARLA和ScenarioRunner进行自动驾驶系统系统级仿真V&V时,能够有效自动生成场景。因此,它们凸显了该方法作为未来ADS V&V方法论中强大工具的潜力。

英文摘要:

With increasing complexity of Automated Driving Systems (ADS), ensuring their safety and reliability has become a critical challenge. The Verification and Validation (V&V) of these systems are particularly demanding when AI components are employed to implement perception and/or control functions. In ESA-funded project VIVAS, we developed a generic framework for system-level simulation-based V&V of autonomous systems. The approach is based on a simulation model of the system, an abstract model that describes symbolically the system behavior, and formal methods to generate scenarios and verify the simulation executions. Various coverage criteria can be defined to guide the automated generation of the scenarios. In this paper, we describe the instantiation of the VIVAS framework for an ADS case study. This is based on the integration of CARLA, a widely-used driving simulator, and its ScenarioRunner tool, which enables the creation of diverse and complex driving scenarios. This is also used in the CARLA Autonomous Driving Challenge to validate different ADS agents for perception and control based on AI, shared by the CARLA community. We describe the development of an abstract ADS model and the formulation of a coverage criterion that focuses on the behaviors of vehicles relative to the vehicle with ADS under verification. Leveraging the VIVAS framework, we generate and execute various driving scenarios, thus testing the capabilities of the AI components. The results show the effectiveness of VIVAS in automatically generating scenarios for system-level simulation-based V&V of an automated driving system using CARLA and ScenarioRunner. Therefore, they highlight the potential of the approach as a powerful tool in the future of ADS V&V methodologies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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