论文标题
使用3D卷积的自我车辆速度估算并引起注意
Ego Vehicle Speed Estimation using 3D Convolution with Masked Attention
论文作者
论文摘要
自我车辆的速度估计对于实现自动驾驶和高级驾驶员援助技术至关重要。由于功能性和遗产问题，传统方法取决于车载传感器通过控制器区域网络总线提取车速。但是，希望拥有不容易外部传感器执行感知任务的模块化系统。在本文中，我们提出了一种具有蒙版意见架构的新颖的3D-CNN，以使用单个前置单眼相机来估算自我车速。为了证明我们方法的有效性，我们对两个公开可用的数据集和Kitti进行了实验。我们还通过将我们的方法与传统的3D-CNN进行比较，证明了蒙版意见的功效。
Speed estimation of an ego vehicle is crucial to enable autonomous driving and advanced driver assistance technologies. Due to functional and legacy issues, conventional methods depend on in-car sensors to extract vehicle speed through the Controller Area Network bus. However, it is desirable to have modular systems that are not susceptible to external sensors to execute perception tasks. In this paper, we propose a novel 3D-CNN with masked-attention architecture to estimate ego vehicle speed using a single front-facing monocular camera. To demonstrate the effectiveness of our method, we conduct experiments on two publicly available datasets, nuImages and KITTI. We also demonstrate the efficacy of masked-attention by comparing our method with a traditional 3D-CNN.
