贝叶斯-高斯混合模型用于增强雷达传感器建模:为ADAS/AD开发提供传感器模拟的数据驱动方法
Kelvin Walenta1,2, Simon Genser1, Selim Solmaz1
1Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria.
Sensors (Basel, Switzerland)
|April 13, 2024
概括
本研究介绍了一种数据驱动的雷达传感器模型,使用高斯混合模型来改进高级驾驶员辅助和自动驾驶 (ADAS/AD) 系统测试. 该模型准确地预测了雷达感知,增强了道路安全模拟.
科学领域:
- 机器人技术和自主系统
- 传感器建模传感器建模
- 道路安全工程 道路安全工程
背景情况:
- 先进的驾驶辅助和自动驾驶 (ADAS/AD) 系统对于道路安全和车辆自动化至关重要.
- 由于系统的复杂性日益增加,对ADAS/AD系统进行全面的测试,特别是在虚拟环境中,是必不可少的.
- 雷达传感器是ADAS/AD的重要组成部分,但准确地建模它们的感知,特别是雷达截面 (RCS),具有挑战性.
研究的目的:
- 开发一个数据驱动的雷达传感器模型,用于ADAS/AD系统的虚拟测试.
- 准确地表示雷达感知,包括雷达截面 (RCS) 和散点分布.
- 为雷达传感器建模创建一个灵活和可扩展的框架.
主要方法:
- 利用高斯混合模型 (GMMs) 来基于数据的建模不同车辆和角度的雷达感知.
- 采用贝叶斯变量方法来自动推断模型复杂度.
- 将GMM扩展为一个全面的雷达传感器模型,包含对象列表,遮蔽效应和基于RCS的可检测性.
主要成果:
- 开发的模型准确地复制了雷达截面 (RCS) 行为和散点分布.
- 在各种模拟驾驶场景中证明了模型的有效性.
- 验证了灵活和模块化框架对特定雷达方面和可扩展性的建模能力.
结论:
- 数据驱动的雷达传感器模型有效地增强了ADAS/AD系统的虚拟测试.
- 拟议的框架为模拟雷达感知提供了一个强大的解决方案,有助于提高道路安全.
- 建议进行进一步的验证,以完善模型的准确性并扩大其在各种场景中的适用性.
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