实现现实的自动驾驶数据集增强:一个真实虚拟融合方法与不一致性缓解
Sukwoo Jung1, Myeongseop Kim1, Jean Oh2
1Contents Convergence Research Center, Korea Electronics Technology Institute, Seongnam-si 13449, Republic of Korea.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
为自动驾驶产生现实的数据集是一项挑战. 本研究介绍了一个真实虚拟融合框架,将真实世界的数据与合成元素相结合,以弥合现实差距,改善自动驾驶系统的对象识别.
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:
- 自动驾驶系统需要广泛的数据集来实现可靠的对象识别.
- 获取用于罕见或危险场景的真实世界数据是昂贵和危险的.
- 纯合成数据通常由于视觉和物理差异而表现出现实差距.
研究的目的:
- 提出一种新的真实虚拟融合框架,用于生成自动驾驶的现实增强图像数据集.
- 解决现实世界数据采集的局限性和合成数据中的现实差距.
- 提高自动驾驶感知模型的概括能力.
主要方法:
- 利用来自韩国K-City的真实世界驾驶数据.
- 使用查找表和微调本地化,与数字双胞胎环境 (Morai Sim) 同步真实数据.
- 将多种虚拟对象注入到真实图像背景中,使用诸如照明匹配等不一致性减轻技术.
主要成果:
- 现实虚拟融合战略有效地弥合了合成数据和真实数据之间的现实差距.
- 该框架提供了一种具有成本效益和安全的数据集增强方法.
- 实验结果表明,感知模型的概括性得到了改进.
结论:
- 拟议的真实虚拟融合框架为创建自动驾驶高保真数据集提供了可行的解决方案.
- 这种方法提高了自动驾驶系统的稳定性和安全性.
- 该方法有助于克服自动驾驶研究中的数据采集挑战.
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