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复习自动驾驶中的多传感器融合
Hui Qian1, Mingchen Wang2, Maotao Zhu1
1School of Automotive Rngieering, Nantong Institute of Technology, Nantong 226002, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
这项调查探讨了自动驾驶中的多模式传感器融合的深度学习. 它涵盖了当前的方法,挑战,如错位,以及未来的方向,包括强大的感知生成人工智能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 多模态传感器融合对于自动驾驶感知至关重要.
- 深度学习方法越来越多地用于整合来自摄像头,LiDAR和雷达的数据.
研究的目的:
- 为最近基于深度学习的传感器融合技术提供结构化的概述.
- 分析架构趋势,学习策略和自动驾驶中的应用.
主要方法:
- 通过建筑范式对融合方法进行分类 (例如,以BEV为中心,跨模式关注).
- 对学习策略和任务适应感知任务的审查.
- 分析主要趋势,如统一的鸟视图 (BEV) 表示和令牌级别对齐.
主要成果:
- 确定了两个关键的架构趋势:统一的BEV表示和代币级跨模式对齐.
- 审查了各种应用程序,包括对象检测,语义细分,行为预测和规划.
- 突出了阻碍现实世界部署的挑战:时空错位,领域转移和可解释性.
结论:
- 未来的研究方向包括扩散模型,Mamba风格的架构和大型视觉语言模型,以实现可扩展和值得信赖的感知.
- 提供了广泛的比较和基准分析,以指导未来的研究.
- 解决当前的挑战是推进强大的自动驾驶系统的关键.
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