自动驾驶中的语义场景完成:一个双流多车辆协作方法
Junxuan Li1, Yuanfang Zhang2, Jiayi Han3
1Guangdong Provincial Engineering Research Center for Optoelectronic Instrument, School of Electronic Science and Engineering (School of Microelectronics), South China Normal University, Foshan 528225, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
本研究引入了用于自动驾驶语义场景完成的双流多车辆 (TSMV) 方法. 通过使用新的注意力模块,TSMV通过解决车辆与车辆之间的通信中的特征错位来提高准确性.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 车辆对车辆 (V2V) 通信通过共享传感器数据来帮助自动驾驶.
- 车辆之间的特征错位导致模糊性,并降低语义场景完成的准确性.
研究的目的:
- 为自动驾驶中协作语义场景完成提出一种新的方法.
- 为了应对V2V通信中特征错位的挑战.
主要方法:
- 引入了双流多车辆 (TSMV) 方法,在两个流中处理协作功能.
- 开发了邻居自我交叉注意力变压器 (NSCAT) 模块,用于查询类似的本地特征,而无需假设同步.
- 从聚合的协作车辆特征生成了一个3D占用地图.
主要成果:
- 与现有方法相比,TSMV方法表现出优越的性能.
- 在V2VSSC和SemanticOPV2V数据集上进行了实验.
- NSCAT模块有效地缓解了因特征错位而产生的问题.
结论:
- TSMV显著提高了协作语义场景完成的准确性.
- 拟议的方法为基于V2V的自动驾驶感知提供了一个强大的解决方案.
- 未来的工作可以探索复杂的驾驶场景的多车辆协作的进一步改进.
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