目的 在智能网联汽车系统开发中，复杂环境下的车道线检测是关键环节之一。目前的车道线检测算法大都基于颜色、灰度和边缘等视觉特征信息，检测准确度受环境影响较大。而车道线的长度、宽度及方向等特征的规律性较强，具有序列化和结构关联的特点，不易受到环境影响。为此，采用视觉信息与空间分布关系相结合的方案，来提高复杂环境下的车道线检测能力。方法 首先针对鸟瞰图中车道线在横向和纵向上分布密度不同的特点，将目标检测算法YOLO v3（you only look once v3）的网格密度由S×S改进为S×2S，得到的YOLO v3（S×2S）更适于小尺寸、大宽高比物体的检测；然后利用车道线序列化和结构相互关联的特点，在双向循环门限单元（bidirectional gated recurrent unit，BGRU）的基础上，提出基于车道线分布关系的车道线检测模型（BGRU-Lane，BGRU-L）。最后利用基于置信度的D-S（Dempster-Shafer）算法融合YOLO v3（S×2S）和BGRU-L的检测结果，提高复杂场景下的车道线检测能力。结果 采用融合了视觉信息和空间分布关系的车道线检测模型，在KITTI（Karlsruhe Institute of Technology and Toyoko Technological Institute）交通数据集上的平均精度均值达到了90.28%，在欧洲卡车模拟2常规场景（Euro Truck Simulator 2 convention，ETS2_conv）和欧洲卡车模拟2复杂场景（Euro Truck Simulator 2 complex，ETS2_complex）下的平均精度均值分别为92.49%和91.73%。结论 通过增大YOLO v3纵向的网格密度，可显著提高模型检测小尺寸、大宽高比物体的准确度；序列化和结构关联是车道线的重要属性，基于空间分布关系的BGRU-L模型的准确度受环境影响较小。两种模型的检测结果在经过D-S融合后，在复杂场景下具有较高的准确度。

Objective Intelligent connected vehicles are an important direction in intelligent transportation in China. In the development of intelligent networked vehicle systems, the detection of lane markings in complex environments is a key link. The safety of drug delivery, meal transport, and medical waste recovery can be guaranteed if the unmanned driving and intelligent network connected vehicle technology can be applied to epidemic prevention and control, especially in the epidemic of COVID-2019. The frequency of contact between medical staff and patients and the risk of cross infection of virus can be reduced. However, the current lane detection algorithms are mostly based on visual feature information, such as color, gray level, and edge. The accuracy of model detection is greatly affected by the environment. This condition makes the accuracy of existing lane detection algorithms difficult to meet the performance requirements of intelligent networked vehicles. The length, width, and direction of lanes have strong regularity, and they have the characteristics of serialization and structure association. These characteristics are not affected by visibility, weather, and obstacles. Vision-based lane detection method has high accuracy in scenes with high definition and without obstacles. For this reason, a lane detection model based on vision and spatial distribution is proposed to eliminate the influence of environment on lane detection. Our research can provide accurate lane information for the development of intelligent driving system. Method When a traffic image set is transformed into a bird's eye view, its original scale changes, and the lane interval is short. The you only look once v3 (YOLO v3) algorithm has significant advantages in speed and accuracy of detecting small objects. Thus, it is used as lane detector in this study. However, the distribution density of lane in the longitudinal direction is greater than that in the horizontal direction. The network structure of YOLO v3 is improved by increasing the vertical detection density to reduce the influence of the change in aspect ratio on target detection. The image is divided into S×2S grids during lane detection, and the obtained YOLO v3 (S×2S) is suitable for lane detection. However, the YOLO v3 (S×2S) lane detection algorithm ignores the spatial information of lane. In the case of poor light and vehicle occlusion, the accuracy of lane detection is poor. Bidirectional gated recurrent unit-lane, (BGRU-L), a lane detection model based on lane distribution law, is proposed by considering that the spatial distribution of lane is unaffected by the environment. This model is used to improve the generalization ability of the lane detection model in complex scenes. This study combines visual information and spatial distribution relationship to avoid the large error of single lane detector and effectively reduce the uncertainty of the system. A confidence-based Dempster-Shafer (D-S) algorithm is used to fuse the detection results of YOLO v3 (S×2S) and BGRU-L detection results for guaranteeing the output of the optimal lane position. Result Karlsruhe Institute of Technology and Toyoko Technological Institute(KITTI) is a commonly used traffic dataset and includes scenes, such as sunny, cloudy, highway, and urban roads. The scenes are increased under complicated working conditions, such as rain, tunnel, and night, to ensure coverage. In this study, the scene in a game, Euro Truck Simulator 2 (ETS2), is used as a supplement dataset. ETS2 is divided into two categories: conventional scene ETS2_conv (sunny, cloudy) and comprehensive scene ETS2_comp (sunny, cloudy, night, rain, and tunnel), to accurately evaluate the effectiveness of the algorithm. On the KITTI dataset, the accuracy of YOLO v3 (S×2S) detection is improved with the increase in detection grid density of YOLO v3, with mean average precision (mAP) of 88.39%. BGRU-L uses the spatial distribution relationship of the lane sequence to detect the location of lane, and the mAP is 76.14%. The reliability-based D-S algorithm is used to fuse the lane detection results of YOLO v3 (S×2S) and BGRU-L, and the final mAP of lane detection is raised to 90.28%. On the ETS2 dataset, the mAP values in the ETS2_conv (Euro Truck Simulator 2 convention, ETS2_conv) and ETS2_complex (Euro Truck Simulator 2 complex, ETS2_complex) scenarios are 92.49% and 91.73%, respectively, by using the lane detection model that combines visual information and spatial distribution relationships. Conclusion This study explores the detection schemes based on machine vision and the spatial distribution relationship of lane to address the difficulty in accurately detecting lanes in complex scenes. On the basis of the characteristics of inconsistent distribution density of lane in bird's eye view, the obtained model, YOLO v3 (S×2S), is suitable for the detection of small-size and large aspect ratio targets by improving the grid density of YOLO v3 model. Experimental results show that the YOLO v3 (S×2S) is significantly higher than YOLO v3 in terms of lane detection accuracy. The lane detection model based on visual information has certain limitations and cannot achieve high-precision detection requirements in complex scenes. However, the length, width, and direction of lane have strong regularity and has the characteristics of serialization and structural correlation. BGRU-L, a lane prediction model based on the spatial distribution of lane, is unaffected by the environment and has strong generalization ability in rain, night, tunnel, and other scenarios. This study uses the D-S algorithm based on confidence to fuse the detection results of YOLO v3 (S×2S) and BGRU-L to avoid the large errors that may exist in the single lane detection model and effectively reduce the uncertainty of the system. The results of lane detection in complex scenes can meet the requirements of intelligent vehicles.