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适应性交叉模式无线化:在不利的情况下增强LiDAR-摄像机融合感知
Muhammad Arslan Ghaffar1,2, Kangshuai Zhang1,2, Nuo Pan1,2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
自动驾驶汽车 (AV) 使用LiDAR和摄像头,但恶劣天气增加了噪音. 我们的自适应式交叉模式排毒 (ACMD) 框架有效地清除传感器数据,以实现更安全的AV.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 传感器融合和感知感应
- 计算机视觉 计算机视觉
背景情况:
- 自动驾驶汽车 (AV) 依赖LiDAR和摄像头传感器来感知环境.
- 雨,雪和雾等恶劣天气条件会降低传感器性能,引入噪音并降低可靠性.
- 当前的排毒方法通常集中在单一的传感器类型上,未能利用互补的多式联网数据.
研究的目的:
- 开发一个先进的无雾化框架,用于在恶劣天气下处理多式联络传感器数据.
- 在具有挑战性的环境条件下提高自动驾驶汽车的可靠性和安全性.
- 为了利用LiDAR和摄像头数据之间的协同关系来增强感知.
主要方法:
- 提出了一个新的自适应式交叉模式退化 (ACMD) 框架.
- 使用特定模式的自排噪编码器和适应式桥梁控制器 (ABC) 进行噪声评估和跨模式指导.
- 集成了一个交叉模态消解 (CMD) 模块,用于对噪声传感器数据进行语义精细化.
主要成果:
- 在PSNR和CD中,ACMD的表现明显超过单模式无噪方法 (Restormer,PathNet,BM3D,PointCleanNet) 的28.2%,33.3%.
- 与最先进的融合模型相比,ACMD在JDE中实现了16.2%的改进.
- 在WeatherKITTI数据集上进行了实验验证,使用合成添加的噪声.
结论:
- 该ACMD框架有效地解决了恶劣天气中的多式联络传感器噪声.
- 提高传感器数据质量直接有助于提高AV可靠性和运行安全.
- 这种方法为在各种环境条件下更强大的自动驾驶系统铺平了道路.
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