论文标题
使用单眼相机的ADA中的车辆间距离和相对速度估计的端到端学习
End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera
论文作者
论文摘要
车辆间距离和相对速度估计是任何ADA的两个基本功能（高级驾驶员 - 辅助系统）。在本文中，我们提出了基于深神经网络端到端训练的单眼摄像头距离和相对速度估计方法。我们方法的主要新颖性是由任意两个时间连续的单眼框架提供的多个视觉线索的整合，其中包括深度特征线索，场景几何线索以及时间光流线线索。我们还提出了一种以车辆为中心的采样机制来减轻运动场中透视失真的影响（即光流）。我们通过轻巧的深神经网络实施该方法。在估计准确性，计算速度和记忆足迹方面，进行了广泛的实验，以证实我们方法比其他最先进方法的优越性能。
Inter-vehicle distance and relative velocity estimations are two basic functions for any ADAS (Advanced driver-assistance systems). In this paper, we propose a monocular camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames, which include deep feature clue, scene geometry clue, as well as temporal optical flow clue. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field (i.e. optical flow). We implement the method by a light-weight deep neural network. Extensive experiments are conducted which confirm the superior performance of our method over other state-of-the-art methods, in terms of estimation accuracy, computational speed, and memory footprint.
