非结构化环境下无人驾驶车辆跟驰方法

提出一种基于多传感器融合的用于非结构化环境下无人驾驶车辆跟驰应用的方法，旨在解决无人驾驶车辆跟驰任务中对引导车辆的局部定位问题，实时获取引导车辆的相对位置和速度.首先以任务需求为导向，通过实车实验结果完成针对非结构化环境特点的传感器选型工作.对所选毫米波雷达和相机设计联合标定流程，实现数据空间融合.设计与目标距离相关的横向距离约束阈值，以及基于历史帧数据变化可靠性分析，解决非结构化环境下雷达数据跳变和虚检现象，实现雷达有效目标提取；考虑相机小孔成像原理，提出大小可变候选车辆检测框生成.最后基于主流的目标检测深度学习框架，设计跟车应用中信息输出流程.实车测试结果表明，该方法可以满足非结构化环境下车辆跟驰应用的基本需求，输出结果有一定的稳定性和精度. 

Study of Autonomous Vehicle Following Method in Unstructed Environment

A kind of multi-sensors fusion method was proposed for autonomous vehicle following application in unstructed environment to solve the problem of the guide vehicle locating in autonomous vehicle following task, and to obtain relative position and speed of guide vehicle in real time. Firstly, oriented by the task requirements, the sensors selection work was completed in term of unstructed environment features by on board experiments results. Then, a joint calibration work-flow was designed for the selected millimeter wave radar (MMW) and camera to realize data spatial fusion. And several works were carried out, including designing the targets distance related transversal distance constraint threshold, analyzing the target data reliability based on annals frame to solve radar data jump and virtual detection phenomenon in unstructed environment and to accomplish radar valid targets extraction, creating the candidate vehicle detection boxes with changeable area based on camera pin-hole imaging principle. Finally, an output information procedure was designed with regard to vehicle following application, based on the mainstream deep learning framework about object detection. On board test results indicate that, the proposed method can satisfy the basic requirements of vehicle following application in most off-road scenes with a certain output stability and accuracy.