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A Sensitivity Analysis Approach for Evaluating a Radar Simulation for Virtual Testing of Autonomous Driving Functions

机译:用于自动驾驶功能虚拟测试的雷达仿真评估的灵敏度分析方法

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摘要

Simulation-based testing is a promising approach to significantly reduce the validation effort of automated driving functions. Realistic models of environment perception sensors such as camera, radar and lidar play a key role in this testing strategy. A generally accepted method to validate these sensor models does not yet exist. Particularly radar has traditionally been one of the most difficult sensors to model. Although promising as an alternative to real test drives, virtual tests are time-consuming due to the fact that they simulate the entire radar system in detail, using computation-intensive simulation techniques to approximate the propagation of electromagnetic waves. In this paper, we introduce a sensitivity analysis approach for developing and evaluating a radar simulation, with the objective to identify the parameters with the greatest impact regarding the system under test. A modular radar system simulation is presented and parameterized to conduct a sensitivity analysis in order to evaluate a spatial clustering algorithm as the system under test, while comparing the output from the radar model to real driving measurements to ensure a realistic model behavior. The presented approach is evaluated and it is demonstrated that with this approach results from different situations can be traced back to the contribution of the individual sub-modules of the radar simulation.
机译:基于仿真的测试是一种有希望的方法,可以大大减少自动驾驶功能的验证工作。环境感知传感器(例如摄像机,雷达和激光雷达)的逼真的模型在此测试策略中起着关键作用。尚不存在验证这些传感器模型的公认方法。特别是雷达传统上一直是最难建模的传感器之一。尽管虚拟测试有望替代真实的测试驱动器,但虚拟测试却很耗时,原因是它们使用计算密集型仿真技术来详细模拟整个雷达系统,以近似电磁波的传播。在本文中,我们介绍了一种用于开发和评估雷达仿真的灵敏度分析方法,目的是确定对被测系统影响最大的参数。给出了模块化雷达系统仿真并对其进行了参数设置,以进行灵敏度分析,以便评估作为被测系统的空间聚类算法,同时将雷达模型的输出与实际驾驶测量结果进行比较,以确保模型表现真实。对所提出的方法进行了评估,并证明了这种方法可以将不同情况下的结果追溯到雷达仿真各个子模块的贡献上。

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