Under bad weather, the ability of intelligent vehicles to perceive the environment accurately is an important research content in many practical applications such as smart cities and unmanned driving. In order to improve vehicle environment perception technology in real hazy scenes, we propose an effective detection algorithm based on Swin Transformer for hazy vehicle detection. This algorithm includes two aspects. First of all, for the aspect of the difficulty in extracting haze features with poor visibility, a dehazing network is designed to obtain high-quality haze-free output through encoding and decoding methods using Swin Transformer blocks. In addition, for the aspect of the difficulty of vehicle detection in hazy images, a new end-to-end vehicle detection model in hazy days is constructed by fusing the dehazing module and the Swin Transformer detection module. In the training stage, the self-made dataset Haze-Car is used, and the haze detection model parameters are initialized by using the dehazing model and Swin-T through transfer learning. Finally, the final haze detection model is obtained by fine tuning. Through the joint learning of dehazing and object detection and comparative experiments on the self-made real hazy image dataset, it can be seen that the detection performance of the model in real-world scenes is improved by 12.5%.

Pedestrian and vehicle detection plays a key role in the safe driving of autonomous vehicles. Although transformer-based object detection algorithms have made great progress, the accuracy of detection in rainy scenarios is still challenging. Based on the Swin Transformer, this paper proposes an end-to-end pedestrian and vehicle detection algorithm (PVformer) with deraining module, which improves the image quality and detection accuracy in rainy scenes. Based on Transformer blocks, a four-branch feature mapping model was introduced to achieve deraining from a single image, thereby mitigating the influence of rain streak occlusion on the detector performance. According to the trouble of small object detection only by visual transformer, we designed a local enhancement perception block based on CNN and Transformer. In addition, the deraining module and the detection module were combined to train the PVformer model through transfer learning. The experimental results show that the algorithm performed well on rainy days and significantly improved the accuracy of pedestrian and vehicle detection.

In autonomous driving, the 3D LiDAR (Light Detection and Ranging) point cloud data of the target are missing due to long distance and occlusion. It makes object detection more difficult. This paper proposes Point Cloud Masked Autoencoder (PCMAE), which can provide pre-training for most voxel-based point cloud object detection algorithms. PCMAE improves the feature representation ability of the 3D backbone for long-distance and occluded objects through self-supervised learning. First, a point cloud masking strategy for autonomous driving scenes named PC-Mask is proposed. It is used to simulate the problem of missing point cloud data information due to occlusion and distance in autonomous driving scenarios. Then, a symmetrical encoder–decoder architecture is designed for pre-training. The encoder is used to extract the high-level features of the point cloud after PC-Mask, and the decoder is used to reconstruct the complete point cloud. Finally, the pre-training method proposed in this paper is applied to SECOND (Sparsely Embedded Convolutional Detection) and Part-A2-Net (Part-aware and Aggregate Neural Network) object detection algorithms. The experimental results show that our method can speed up the model convergence speed and improve the detection accuracy, especially the detection effect of long-distance and occluded objects.