论文标题
使用单眼摄像机的自我运动和周围的车辆状态估计
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
论文作者
论文摘要
了解自动运动和周围车辆状态对于实现自动驾驶和高级驾驶援助技术至关重要。解决此问题的典型方法使用多个传感器（例如LiDAR，相机和雷达）的融合来识别周围的车辆状态，包括位置，速度和方向。这种感应方式过于复杂，生产个人使用车辆。在本文中，我们提出了一种新型的机器学习方法，以使用单眼相机来估计自我动机和周围的车辆状态。我们的方法基于三个深神经网络的组合，以估算一系列图像序列的3D车辆边界框，深度和光流。本文的主要贡献是一种新的框架和算法，该算法集成了这三个网络，以估算自我运动和周围的车辆状态。为了实现更准确的3D位置估计，我们实时解决了地面平面校正。通过实验评估将我们的结果与其他传感器（包括Can-Bus和LiDAR）提供的基础真实数据进行比较，证明了所提出方法的功效。
Understanding ego-motion and surrounding vehicle state is essential to enable automated driving and advanced driving assistance technologies. Typical approaches to solve this problem use fusion of multiple sensors such as LiDAR, camera, and radar to recognize surrounding vehicle state, including position, velocity, and orientation. Such sensing modalities are overly complex and costly for production of personal use vehicles. In this paper, we propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera. Our approach is based on a combination of three deep neural networks to estimate the 3D vehicle bounding box, depth, and optical flow from a sequence of images. The main contribution of this paper is a new framework and algorithm that integrates these three networks in order to estimate the ego-motion and surrounding vehicle state. To realize more accurate 3D position estimation, we address ground plane correction in real-time. The efficacy of the proposed method is demonstrated through experimental evaluations that compare our results to ground truth data available from other sensors including Can-Bus and LiDAR.
