超视界路况下网联汽车的辅助驾驶方法

针对车辆前方超视界路况不易预测、驾驶员易因路况不明而错误操作车辆导致失稳等危险事故的情况，提出了一种超视界路况下的网联汽车辅助驾驶方法。设计了基于车联网实时通信技术的网联汽车辅助驾驶系统，它主要由云端服务、共享驿站和车载单元组成；研究了行驶车辆的路况信息识别方法，包括路面附着系数识别、道路曲率计算和路面坡度测量；研究了基于多车辆测量数据的路况信息融合与校正方法，建立了基于三自由度车辆的刚体力学的弯道安全车速计算模型。实车实验结果表明，所提方法能够准确测量车辆前方超视界的路况信息，并可使驾驶员提前、准确地获知超视界路况信息，特别是弯道路况信息。

Assisted Driving Method of Network-connected Vehicle in Out-of-sight Roads

A assisted driving method of network-connected vehicle was proposed for solving the problems that out-of-sight road conditions in front of vehicles which were not easy to predict, and the drivers were prone to dangerous accidents such as misoperation of vehicles due to unclear road conditions. A assisted driving system of network-connected vehicle was designed based on real-time communication technology of vehicle networking. The system was mainly composed of cloud service, shared stations and vehicle-mounted units. The methods of road condition information identification was studied based on moving vehicles, including road adhesion coefficient identification, road curvature calculation and pavement slope measurement. Then the fusion and correction method of road condition information was studied based on multi-vehicle measurement data, and a calculation model of the safe driving speed on a bend was established based on three-degree-of-freedom vehicle model. The real vehicleing test results show that the proposed method may accurately measure the road condition information of the roads out of sight, and may enable the driver to obtain the out-of-sight road traffic informations in advance and accurately, especially the road condition information of the bend.