开发一种有效的与腐败相关的基于场景的测试方法,用于验证自动驾驶系统的稳定性和增强感知系统
Huang Hsiang1, Yung-Yuan Chen1
1Department of Electrical Engineering, National Taipei University, New Taipei City 23741, Taiwan.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
本研究引入了一个模拟过程,以测试和改进自动驾驶汽车传感器系统对各种腐败的稳定性. 它增强了对象检测模型.
科学领域:
- 计算机视觉 计算机视觉
- 自主系统 自主系统
- 机器人技术 机器人技术 机器人技术
背景情况:
- 基于传感器的感知系统对于自动驾驶汽车 (AV) 的安全性和功能至关重要.
- 在部署之前,对这些系统对现实世界的腐败进行强有力的验证是必不可少的.
- 现有的验证方法可能缺乏对各种腐败类型的全面覆盖.
研究的目的:
- 提出和评估基于模拟的过程,以验证和提高AV传感器感知系统的稳定性.
- 在模拟交通环境中解决安全关键的腐败问题.
- 提高对象检测模型对天气和噪音相关损坏的耐受性.
主要方法:
- 开发了一种方法和工具,用于生成模仿现实世界交通的多样化,基于场景的腐败.
- 实施了腐败相似性过算法,以识别代表性腐败类型并减少测试冗余.
- 在对象检测模型上进行了漏洞分析,并创建了增强的训练数据集.
主要成果:
- 拟议的过程允许高效的测试场景创建,减少测试时间和全面的场景覆盖.
- 漏洞分析发现了模型的弱点,从而提高了对象检测对天气和噪音的耐受性.
- 案例研究表明了稳定性验证和增强程序的可行性和有效性.
结论:
- 基于模拟的方法有效地验证和增强自动驾驶汽车基于传感器的感知系统的稳定性.
- 该方法提供了一个可扩展和有效的手段,以确保面对环境腐败的系统可靠性.
- 对"相似重叠值"等参数设置的进一步调查可以优化场景复杂性和成本.
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