3D pedestrian detection is an important problem in most intelligent transportation fields. Pedestrian detection in 3D under crowding scenes is big challenge of most detection models based on deep learning networks. Methods usually used to produce the 3D location of pedestrians could be split into two parts, i.e., 2D pedestrian detection and depth map prediction. The poor coupling between these two tasks results in inferior performance of such methods. In this paper, we propose a light-weight 3D pedestrian detection model based on binocular images which directly generates 3D bounding boxes of pedestrians. It is designed to alleviate the negative impact on occlusion problem by applying multi-scale stereo features fusion and data augmentation strategy. The model we proposed outperforms most 3D pedestrian detection methods on KITTI dataset.
Automatic safe lane changing is the key to the realization of unmanned vehicles. To accurately identify the lane changing state of driving vehicles to ensure driving safety, this paper establishes a vehicle automatic lane changing behavior recognition model based on the multi-class support vector machine. This paper selects vehicle trajectory data from the NGSIM data set for classification processing and uses genetic algorithm optimized particle swarm optimization (GA-PSO) to optimize and calibrate the penalty parameter C and the kernel parameter g in the multi-class support vector machine model. Using sample data to train and test lane-changing behavior recognition models and the research shows that the model can well recognize the behavior of the vehicle during the automatic lane changing process and provide support for the study of the vehicle lane changing phase.
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