One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle’s behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle in order to be able to make its driving decisions. In this paper we present an approach for the identification of obstacles and extraction of class, position, depth and motion information from these objects that employs data gained exclusively from passive vision. We use a convolutional neural network for the obstacles detection, optical flow for the analysis of movement of the detected obstacles, both in relation to the direction and in relation to the intensity of the movement, and also stereo vision for the analysis of distance of obstacles in relation to the vehicle. We performed our experiments on two different datasets, and the results obtained showed a good efficacy from the use of depth and motion patterns to assess the obstacles’ potential threat status.

智能车辆导航系统中最相关的任务之一是障碍物的检测。用于导航目的的视觉感知系统识别障碍很重要，并且该系统可以提取可能影响车辆行为的重要信息，无论它会为人类驾驶员发出警报还是在自动驾驶车辆上引导，这一点也很重要。为了能够做出自己的驾驶决策。在本文中，我们提出了一种识别障碍物并从这些物体中提取类别，位置，深度和运动信息的方法，该方法采用了仅从被动视觉获得的数据。我们使用卷积神经网络进行障碍物检测，使用光流分析检测到的障碍物的运动，不仅涉及运动的方向和强度，还涉及立体视觉，用于分析障碍物相对于车辆的距离。我们在两个不同的数据集上进行了实验，获得的结果表明，使用深度和运动模式来评估障碍物的潜在威胁状态具有良好的效果。