通过平滑的内核回归计算,用单个车载摄像头估计附近车辆的速度
Mónica López-Pola1,2, Iván García-Aguilar1,2, Jorge García-González1,2
1IBIMA Plataforma BIONAND, C/Doctor Miguel Díaz Recio, 28, Málaga 29010, Spain.
International journal of neural systems
|December 26, 2025
概括
本研究介绍了一种仅使用摄像头的系统,用于通过角度宽度和内核光滑来估计车辆速度. 该方法为驾驶员辅助系统提供了低成本的替代方案.
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
- 汽车工程 汽车工程
背景情况:
- 估计车辆速度对于高级驾驶辅助系统 (ADAS) 至关重要.
- 现有的方法通常依赖于昂贵或复杂的传感器套件.
- 为了成本效益和更广泛的采用,需要实时,仅摄像头解决方案.
研究的目的:
- 开发和评估一个实时的,仅用于摄像头的管道,用于估计附近车辆的相对速度.
- 提供低成本的替代品或补充活动传感器,如LiDAR或雷达.
- 评估建议方法的准确性和局限性.
主要方法:
- 使用卷积神经网络 (CNN),特别是YOLOv8.8进行车辆检测和跟踪.
- 基于界限框角宽度,取决于类的 priors 和相机内在特征的距离估计.
- 纳达拉亚-沃森内核光滑应用于距离序列,通过分析差异化估计相对速度.
主要成果:
- 该系统只使用单眼相机输入成功估计了车辆距离和相对速度.
- 对合成数据的评估揭示了远离图像中心的位置偏差.
- 由于平滑内核,观察到稳定性和延迟之间的权衡.
结论:
- 拟议的探测器不可知管道有效地通过单眼相机数据估计车辆的速度.
- 这种方法为驾驶员辅助应用提供了可行的,低成本的解决方案.
- 在现实场景中进行进一步的验证是必要的,以解决发现的局限性.
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