Despite the presence of the classification task in many different benchmark
datasets for perception in the automotive domain, few efforts have been
undertaken to define consistent classification requirements. This work
addresses the topic by proposing a structured method to generate a
classification structure. First, legal categories are identified based on
behavioral requirements for the vehicle. This structure is further
substantiated by considering the two aspects of collision safety for objects as
well as perceptual categories. A classification hierarchy is obtained by
applying the method to an exemplary legal text. A comparison of the results
with benchmark dataset categories shows limited agreement. This indicates the
necessity for explicit consideration of legal requirements regarding
perception.

提出了一种结构化的方法来生成分类结构，通过考虑车辆的行为需求来确定合法类别，进而通过考虑对象的碰撞安全和感知类别的两个方面来进一步验证该结构。将该方法应用于一个示例性的法律文本，获取到了一个分类层次结构；将结果与基准数据集的类别进行比较，结果显示一致性有限。这表明有必要明确考虑感知方面的法律要求。

建立自动驾驶的系统分类需求

Towards Establishing Systematic Classification Requirements for  Automated Driving

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.

为了自动驾驶释放，需要有效的测试策略和适当的测试方法。本研究提出了一个新的方法来解决感知模块的要求问题，并针对碰撞安全在高速公路领域进行了示例应用。通过功能场景的拆分和限制交通规则，得出了感知的相关性标准。这些结果可应用于离线测试和验证。