高效和实时感知:对自动驾驶中的端到端基于事件的对象检测进行了一项调查
Kamilya Smagulova1, Ahmed Elsheikh2, Diego A Silva1
1Communication and Computing Systems Lab, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.
Frontiers in robotics and AI
|November 19, 2025
概括
本调查探讨了自动驾驶的端到端基于事件的对象检测,详细介绍了硬件,数据集和算法. 它强调了挑战,并提出了对事件摄像机处理的未来研究方向.
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:
- 自动驾驶依赖于视觉传感器来确保安全和可访问性.
- 基于事件的摄像机比传统摄像机提供了高动态范围和低功耗等优势.
- 事件数据的稀疏和异步性质带来了独特的处理挑战.
研究的目的:
- 为自动驾驶提供基于事件的端到端对象检测的全面调查.
- 涵盖传感/处理硬件,数据集和算法.
- 为了公平比较,确定当前评估实践中的缺陷.
主要方法:
- 对基于事件的自动驾驶视觉现有文献的调查.
- 分析算法,包括密集,尖端和基于图的神经网络.
- 对特定硬件和数据集的最先进模型的系统级吞吐量评估.
主要成果:
- 基于事件的摄像头具有独特的处理挑战,需要专门的算法.
- 从基于框架的数据调整的现有模型往往表现不佳.
- 较新的专业算法显示出前景,但需要进一步开发和验证.
- 系统级吞吐量在RTX 4090 GPU上使用GEN1和1MP数据集进行了评估.
结论:
- 需要强大的专用算法来充分利用基于事件的摄像头数据来实现自动驾驶.
- 标准化的评估方法对于比较不同的方法至关重要.
- 未来的研究应该专注于提高算法成熟度,准确性和高效的硬件-软件联合设计.
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