Considerable study has already been conducted regarding autonomous driving in modern era. An autonomous driving system must be extremely good at detecting objects surrounding the car to ensure safety. In this paper, classification, and estimation of an object's (pedestrian) position (concerning an ego 3D coordinate system) are studied and the distance between the ego vehicle and the object in the context of autonomous driving is measured. To classify the object, faster Region-based Convolution Neural Network (R-CNN) with inception v2 is utilized. First, a network is trained with customized dataset to estimate the reference position of objects as well as the distance from the vehicle. From camera calibration to computing the distance, cutting-edge technologies of computer vision algorithms in a series of processes are applied to generate a 3D reference point of the region of interest. The foremost step in this process is generating a disparity map using the concept of stereo vision.

翻译：在现代自动驾驶领域已开展了大量研究。为确保安全，自动驾驶系统必须具备出色的车辆周边目标检测能力。本文针对自动驾驶场景，研究了目标（行人）的分类与位置估计（基于自车三维坐标系），并测量了自车与目标之间的距离。在目标分类方面，采用基于Inception v2的快速区域卷积神经网络（R-CNN）。首先通过定制数据集训练网络，以估计目标的参考位置及其与车辆的间距。从相机标定到距离计算，本工作应用了一系列计算机视觉算法中的前沿技术，通过多步骤处理生成感兴趣区域的三维参考点。该流程的首要步骤是利用立体视觉概念生成视差图。