利用激光点云的智能网联汽车自主换道横向避障

Intelligent connected vehicle autonomous lane changing and lateral obstacle avoidance using laser point cloud

通过智能网联汽车自主换道横向避障设计,提高车辆自主驾驶安全性能,提出基于激光点云的智能网联汽车自主换道横向避障方法,构建基于全激光雷达的无人驾驶系统的汽车行驶环境激光点云数据采集模型,通过激光雷达激光点云图像检测方法实现对智能网汽车行驶环境感知。结合障碍物检测、车道线检测、可行驶区域检测的方法,分析智能网联下汽车换道障碍物和车道的相关性特征量,通过激光点云的自主寻优方法寻找边界点直到搜索路径达到阈值,再结合直线道路模型和车道线连续性原则实现对汽车自主换道横向避障规划设计。仿真结果表明,采用该方法进行汽车自主环控避障,在5 s时达到收敛状态,障碍物坐标识别误差距离低于0.7,并且该方法的识别精度达到了0.970以上,提高了车道信息的感知能力,准确高效地估计可行驶区域,提高车辆避障能力。

Through the design of autonomous lane changing lateral obstacle avoidance for intelligent connected vehicles,the safety performance of vehicle autonomous driving is improved.A laser point cloud based intelligent connected vehicle autonomous lane changing lateral obstacle avoidance method is proposed,and a vehicle driving environment excitation point cloud data collection model based on an all laser radar unmanned driving system is constructed.The perception of the intelligent network vehicle driving environment is achieved through the laser radar laser point cloud image detection method.Combining the methods of obstacle detection,lane line detection,and driving area detection,this paper analyzes the correlation characteristics of obstacles and lanes for vehicle lane changing under intelligent network,searches for boundary point through the autonomous optimization method of laser point cloud until the search path reaches the threshold,and then realizes the planning and design of lateral obstacle avoidance for vehicle autonomous lane changing based on the straight line road model and the continuity principle of lane lines.The simulation results show that the proposed method achieves convergence in 5s,the obstacle coordinate identification error distance is less than 0.7,and the identification accuracy of the proposed method is more than 0.970,which improves the perception ability of lane information,accurately and efficiently estimates the driving area,and improves the obstacle avoidance ability of vehicles.