自动驾驶在拥堵路段的道路几何信息估计

Estimation of Road Geometric Information for Congested Roads by Autonomous Driving

道路几何信息是自动驾驶系统中重要的信息来源,也是后续路径规划的关键参考信息之一。该研究针对城市内车道线遮挡及多路径效应导致的全球定位系统失效等问题,提出了一种基于前车信息的道路几何估计方法。通过对当前车辆、前车以及道路之间关系的建模,获得了系统的运动模型和观测模型。采用无损卡尔曼滤波框架对观测到的前车相对位置、相对速度、相对角度和本车角速度进行滤波处理,估计出当前车道的曲率参数。在仿真软件Car learning to Act(Carla)上的实验结果表明,相比地图匹配方法,在无法获取车道线目标及精确定位信息的情况下,该方法道路几何精度得到了显著提升。

Road geometry information is an important information source in the autonomous driving perception system, which also plays an important role in the subsequent route planning. To realize the autonomous driving perception while the lane line is invisible and the signal of global positioning system is not available, a road geometry estimation based on the leading vehicle is proposed in this work. By modeling the relationship between the current vehicle, the preceding vehicle and the road, we can obtain the system motion model and the observation model. Then, the unscented Kalman filter framework is applied to filter the observed relative position, relative speed, and relative angel of the preceding vehicle and the angular velocity of the host vehicle, for estimating the curvature of current road. The experimental results on the simulation software car learning to act(Carla) showed that, in congested scenarios where lane line targets cannot be obtained and host vehicle cannot be accurately located, road geometry accuracy by the proposed method can be greatly improved in comparison with conventional map matching methods.