Towards a New Approach for Reducing the Safety Validation Effort of Driving Functions Using Prediction Divergence Current Approach and Challenges

Daniel Betschinske, Malte Schrimpf, Moritz Lippert, Steven Peters
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Abstract

An essential component in the approval of advanced driver assistance systems (ADAS) and automated driving systems (ADS) is the quantification of residual risk, which demonstrates that hazardous behavior (HB) occurs less frequently than specified by a corresponding acceptance criterion. In the case of HB with high potential impact severity, only very low accepted frequencies of occurrence are tolerated. To avoid uncertainties due to abstractions and simplifications in simulations, the proof of the residual risk in systems such as advanced emergency braking systems (AEBS) is often partially or entirely implemented as system level field test. However, the low rates and high confidence required, common for residual risk demonstrations, result in a significant disadvantage of these field tests: the long driving distance required. In this publication, the prediction divergence principle (PDP) is presented as an approach that has the potential to reduce the testing effort in the future, especially for systems based on the sense-plane-act structure. By continuously monitoring the prediction divergence, the approach provides essential information about the predictive performance of the system under test (SUT). In addition to the elaborated concept, this paper focuses on the mathematical decomposition of the HB into the false prediction (FPr) of the SUT and the probability that this FPr causes the HB. The approach is illustrated using the example of an AEBS. Furthermore, the prerequisites for applying the approach and the associated test reduction are derived using simplified models. Finally, the steps that must be investigated before the theoretical approach can be applied in practice are derived.
利用预测分歧减少驾驶功能安全验证工作量的新方法 当前方法与挑战
高级驾驶辅助系统(ADAS)和自动驾驶系统(ADS)审批的一个重要组成部分是剩余风险量化,它表明危险行为(HB)的发生频率低于相应的接受标准。对于潜在影响严重程度较高的危险行为,只能容忍极低的可接受发生频率。为了避免模拟中的抽象和简化造成的不确定性,高级紧急制动系统(AEBS)等系统的残余风险证明通常部分或全部作为系统级现场测试来实施。然而,残余风险论证通常要求的低速率和高置信度导致了这些现场测试的一个显著缺点:所需的驾驶距离较长。在本出版物中,预测发散原理(PDP)作为一种方法被提出,它有可能在未来减少测试工作量,特别是对于基于感知-平面-作用结构的系统。通过持续监测预测偏差,该方法可提供有关被测系统(SUT)预测性能的重要信息。除阐述概念外,本文还重点介绍了将 HB 分解为 SUT 的错误预测 (FPr) 和 FPr 导致 HB 的概率的数学方法。本文以 AEBS 为例对该方法进行了说明。此外,还使用简化模型推导了应用该方法的先决条件和相关的测试缩减。最后,还得出了在实际应用该理论方法之前必须研究的步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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