Having efficient testing strategies is a core challenge that needs to be overcome for the release of automated driving. This necessitates clear requirements as well as suitable methods for testing. In this work, the requirements for perception modules are considered with respect to relevance. The concept of relevance currently remains insufficiently defined and specified. In this paper, we propose a novel methodology to overcome this challenge by exemplary application to collision safety in the highway domain. Using this general system and use case specification, a corresponding concept for relevance is derived. Irrelevant objects are thus defined as objects which do not limit the set of safe actions available to the ego vehicle under consideration of all uncertainties. As an initial step, the use case is decomposed into functional scenarios with respect to collision relevance. For each functional scenario, possible actions of both the ego vehicle and any other dynamic object are formalized as equations. This set of possible actions is constrained by traffic rules, yielding relevance criteria. As a result, we present a conservative estimation which dynamic objects are relevant for perception and need to be considered for a complete evaluation. The estimation provides requirements which are applicable for offline testing and validation of perception components. A visualization is presented for examples from the highD dataset, showing the plausibility of the results. Finally, a possibility for a future validation of the presented relevance concept is outlined.

翻译：高效测试策略是实现自动驾驶商业化部署所需攻克的核心挑战。这就要求建立明确的测试需求及相应方法体系。本文从相关性视角审视感知模块的测试需求，然而当前"相关性"概念仍缺乏明确定义与规范。为此，我们提出一种创新方法论，以高速公路场景下的碰撞安全为例进行示范性应用。基于通用系统与用例规范，推导出对应的相关性概念框架。据此，非相关目标被定义为：在考虑全部不确定性因素后，不会限制自车可用安全动作集合的目标对象。作为初始步骤，我们将用例按碰撞相关性分解为功能性场景。针对每个功能性场景，自车及所有动态目标的潜在运动均被形式化为数学方程。通过引入交通规则约束该运动集合，最终推导出相关性判据。研究最终提出一种保守估计方法，用于判别哪些动态目标与感知相关且须纳入完整评估体系。该估计方法为感知组件的离线测试与验证提供了明确需求规范。通过高D数据集（highD）的实例可视化验证，展示了所得结果的合理性。最后，本文概述了该相关性概念框架的未来验证可能性。