The invention discloses a real-time target detection, tracking and
motion prediction method for automatic driving, and belongs to the technical field of automatic driving. The method mainly comprises the following steps: 1, receiving three-dimensional
point cloud data and corresponding two-dimensional image data; 2, extracting feature representation of the
point cloud and the image; 3, performing multi-
modal information fusion on the image features and the
point cloud features by adopting a step-by-step fusion mode to obtain self-adaptive fused
voxel features; 4, according to the self-adaptive fused
voxel features, a target detection output head, a behavior tracking output head and a behavior prediction output head are adopted for analysis, and a detection result, a tracking result and a prediction result of the moving objects around the vehicle are obtained. According to the method, a single
convolutional neural network is utilized, and three-dimensional point
cloud data and corresponding two-dimensional image information are combined, so that efficient and accurate three-dimensional
object detection, tracking and
motion prediction are realized. Through a unified
network architecture, traditional multi-stage processes are integrated into one model, and the efficiency and performance of the
system are improved.