AFTR:基于自适应融合变压器的3D物体检测的强度多传感器融合模型
Yan Zhang1, Kang Liu1, Hong Bao2
1School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
我们介绍了自适应融合变压器 (AFTR),这是一个用于自动驾驶多传感器数据融合的新型框架. AFTR通过适应性处理传感器错位和动态场景来提高3D对象检测的准确性和稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 像LiDAR和摄像头这样的多模式传感器对于自动驾驶至关重要.
- 当前的融合方法与不一致的数据表示和动态场景错位作斗争.
- 现有的方法要么忽略错位问题,要么产生高计算成本.
研究的目的:
- 开发一个端到端的多传感器融合框架,解决当前方法的局限性.
- 通过先进的传感器融合,提高自动驾驶系统的准确性,效率和稳定性.
主要方法:
- 提出了自适应融合变压器 (AFTR),这是一个基于变压器的框架.
- 引入了适应性空间交叉注意力 (ASCA) 机制,用于局部特征关联和错位减少.
- 实现了时空自我注意力 (STSA) 机制,以减轻动态场景移位.
主要成果:
- 在nuScenes 3D对象检测任务中实现了最先进的 (SOTA) 性能 (74.9%的NDS,73.2%的mAP).
- 对传感器错位表现出强大的稳定性,在轻微噪音下NDS下降仅为0.2%.
- 通过废除研究验证了单个AFTR组件的有效性.
结论:
- 拟议的AFTR框架为多传感器数据融合提供了一个准确,高效和强大的解决方案.
- AFTR有效地应对自动驾驶中传感器错位和动态环境所带来的挑战.
- 该框架显示了提高自动驾驶系统可靠性的巨大潜力.
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