CPD-KD:一个合作感知网络,通过知识蒸来融合差异特征
Caizhen He1, Hai Wang2, Tong Luo3
1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang, 212013, China.
Scientific reports
|July 27, 2025
概括
智能互联汽车使用路边传感器和5G来增强环境感知. 一个新的合作感知网络通过知识蒸 (CPD-KD) 来实现差异特征融合,通过合并车辆和基础设施数据来提高准确性.
科学领域:
- 智能运输系统 智能运输系统
- 计算机视觉 计算机视觉
- 无线通信无线通信
背景情况:
- 智能互联汽车 (ICV) 的环境感知传统上依赖于车载传感器,限制了范围和准确性.
- 当前的方法在合并车辆和基础设施数据时,遇到模糊的功能和信息丢失.
- 使用路边单位 (RSU) 和5G的合作感知增强了ICV的情境意识.
研究的目的:
- 通过知识蒸 (CPD-KD) 提出一个新的合作感知网络,用于通过知识蒸 (CPD-KD) 融合不一致的功能.
- 解决传统合作感知方法中模糊特征和信息丢失的挑战.
- 提高智能互联汽车对环境感知的准确性和效率.
主要方法:
- 开发了一个基于Sparse卷积的知识蒸网络 (SKD),以使用融合的视点数据来改进单个视点云功能.
- 引入了异常特征注意力融合模块,以有效地整合车辆和基础设施传感之间的差异信息.
- 在真实世界 (DAIR-V2X) 和模拟 (V2X-Set) 数据集上验证了CPD-KD算法.
主要成果:
- 该SKD网络成功地缓解了点云数据中目标特征的模糊.
- 不一致特征注意力融合模块提高了车辆基础设施数据融合的合作效率.
- CPD-KD在合作感知的准确性方面表现出显著的改善.
结论:
- 拟议的CPD-KD算法有效地提高了智能互联汽车的合作感知精度.
- 知识蒸和差异特征融合是克服当前合作感知系统局限性的关键.
- 在联网汽车环境中,CPD-KD为更强大,更准确的环境传感提供了一个有前途的解决方案.
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