基于EuroNCAP和US NCAP场景的主动安全算法在虚拟测试环境中的大规模评估——一个工业案例研究

C. Berger, D. Block, C. Hons, Stefan Kühnel, André Leschke, D. Plotnikov, Bernhard Rumpe
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引用次数: 4

摘要

背景:最近,来自欧洲新车评估计划(EuroNCAP)等组织的测试协议扩展到主动安全系统。目的:自动紧急制动(AEB)/前方碰撞警告(FCW)系统的官方EuroNCAP测试协议明确定义了被测车辆(VUT)允许在其横向位置变化的程度。此外,美国新车评估计划(US NCAP)测试协议具有更广泛的公差范围。汽车oem的目标是了解这些允许的变化对车辆整体性能的影响。方法:概述了一种基于模拟的方法,允许对此类影响进行系统、大规模的分析,以有效地规划耗时且资源密集的真实世界车辆测试。我们的模型通过对EuroNCAP的动态CCRm和CCRb场景(包括采用USNCAP参数的场景)进行建模和进行3000多次模拟运行,允许对AEB算法进行深入分析。结果:我们对涉及动态参与者的此类测试程序的结构化分析是相关工业环境中的首次此类分析。在美国NCAP条件下发现了一些异常情况,以支持实际测试运行。因此,我们可以证明所提议的方法支持AEB消费者测试和规模中所有可能的场景,因为我们必须及时处理近似。7.7GB模拟数据。结论:为了达到预期的性能,并从功能的角度研究系统在潜在误用情况下的行为,大规模的、基于模型的仿真补充了传统的试验场测试。
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Large-Scale Evaluation of an Active Safety Algorithm with EuroNCAP and US NCAP Scenarios in a Virtual Test Environment -- An Industrial Case Study
Context: Recently, test protocols from organizations like European New Car Assessment Programme (EuroNCAP) were extended to also cover active safety systems. Objective: The official EuroNCAP test protocol for Autonomous Emergency Braking (AEB)/Forward Collision Warning (FCW) systems explicitly defines to what extent a Vehicle-Under-Test (VUT) is allowed to vary in its lateral position. In addition, the United States New Car Assessment Programme (US NCAP) test protocol has broader tolerance ranges. The goal for automotive OEMs is to understand the impact of such allowed variations on a the overall vehicle's performance. Method: A simulation-based approach is outlined that allows systematic, large-scale analysis of such influences to effectively plan time-consuming and resource-intense real-world vehicle tests. Our models allow a profound analysis of an AEB algorithm by modeling and conducting more than 3,000 simulation runs with EuroNCAP's dynamic CCRm and CCRb scenarios including those with adopted USNCAP parameters. Results: Our structured analysis of such test procedures involving dynamic actors is the first of its kind in a relevant industrial setting. Several anomalies were unveiled under US NCAP conditions to support real-world test runs. Hence, we could show that the proposed method supports all possible scenarios in AEB consumer tests and scales as we had to timely process approx. 7.7GB of simulation data. Conclusion: To achieve the expected performance and to study a system's behavior in potential misuse cases from a functional point of view, large scale, model-based simulations complement traditional testing on proving ground.
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