论文标题
使用现象学传感器模型对自动化车辆的传感器覆盖率进行系统分析
Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models
论文作者
论文摘要
本文的目的是对自动车辆的传感器覆盖率进行系统分析。由于可能的流量情况无限数量，因此必须在自动化车辆的安全评估中使用一系列要测试的方案。本文介绍了如何使用现象学传感器模型来识别系统特定相关方案。在自动驾驶中，主要使用以下传感器：相机，超声波，\雷达和\ lidarohne。基于文献，已经为四种传感器类型开发了现象学模型，这些模型考虑了现象，例如环境影响，传感器特性和要检测到的对象的类型。这些现象学模型比简单的理想传感器模型具有明显更高的可靠性，并且比实际的物理传感器模型需要较低的计算成本，这代表了对传感器覆盖率进行系统研究的最佳折衷。模拟显示了不同系统配置之间的显着差异，因此支持系统特定的相关方案选择自动车辆安全评估。
The objective of this paper is to propose a systematic analysis of the sensor coverage of automated vehicles. Due to an unlimited number of possible traffic situations, a selection of scenarios to be tested must be applied in the safety assessment of automated vehicles. This paper describes how phenomenological sensor models can be used to identify system-specific relevant scenarios. In automated driving, the following sensors are predominantly used: camera, ultrasonic, \radar and \lidarohne. Based on the literature, phenomenological models have been developed for the four sensor types, which take into account phenomena such as environmental influences, sensor properties and the type of object to be detected. These phenomenological models have a significantly higher reliability than simple ideal sensor models and require lower computing costs than realistic physical sensor models, which represents an optimal compromise for systematic investigations of sensor coverage. The simulations showed significant differences between different system configurations and thus support the system-specific selection of relevant scenarios for the safety assessment of automated vehicles.
