Optical flow and disparity are two informative visual features for autonomous driving perception. They have been used for a variety of applications, such as obstacle and lane detection. The concept of "U-V-Disparity" has been widely explored in the literature, while its counterpart in optical flow has received relatively little attention. Traditional motion analysis algorithms estimate optical flow by matching correspondences between two successive video frames, which limits the full utilization of environmental information and geometric constraints. Therefore, we propose a novel strategy to model optical flow in the collision-free space (also referred to as drivable area or simply freespace) for intelligent vehicles, with the full utilization of geometry information in a 3D driving environment. We provide explicit representations of optical flow and deduce the quadratic relationship between the optical flow component and the vertical coordinate. Through extensive experiments on several public datasets, we demonstrate the high accuracy and robustness of our model. Additionally, our proposed freespace optical flow model boasts a diverse array of applications within the realm of automated driving, providing a geometric constraint in freespace detection, vehicle localization, and more. We have made our source code publicly available at https://mias.group/FSOF.

翻译：光流与视差是自动驾驶感知中两种富有信息的视觉特征，已被广泛应用于障碍物检测、车道线检测等任务。"U-V-视差"概念在文献中已被广泛探讨，而其对应在光流领域的研究却相对较少。传统运动分析算法通过匹配连续两帧视频帧之间的对应点来估计光流，这限制了环境信息与几何约束的充分利用。为此，我们提出一种新策略，在智能车辆的无碰撞空间（即可行驶区域，简称自由空间）中建立光流模型，充分挖掘三维驾驶环境中的几何信息。我们给出了光流的显式表达，并推导出光流分量与垂直坐标之间的二次关系。通过在多个公开数据集上的大量实验，验证了所提模型的高精度与鲁棒性。此外，我们提出的自由空间光流模型在自动驾驶领域具有广泛的应用前景，可为自由空间检测、车辆定位等任务提供几何约束。相关源代码已开源至 https://mias.group/FSOF。