The invention discloses a method for realizing scene structure prediction, target detection and lane level positioning, and relates to the fields of automatic driving, deep learning, computer vision and the like. The method comprises the following steps: firstly, constructing a neural network for lane-level positioning, scene structure prediction and target detection, and constructing a loss function mathematical model through loss between a scene structure prediction value and a target detection prediction value and a true value thereof; making a data set through an image and a map, and training the network; deploying the network on an vehicle to output a detection result; and finally, carrying out retrieval matching on the output scene structure and a map through a matching method, correcting the positioning error of the vehicle, and realizing lane-level positioning. According to the network, a data set can be made through images and maps, closed-loop training is carried out on the network, and scene structure prediction, a target detection function and lane level positioning can be completed only through image information and map information. The road structure contained in the scene structure prediction result can be used in automatic driving.