As the core technology of an environmental perception system, object detection has received more and more attention and has become a hot research direction for intelligent driving vehicles. The CNN–Transformer hybrid model has poor generalization ability, making it difficult to meet the detection requirements for small objects in complex scenes. We propose a novel convolutional neural network (CNN)–Transformer Adaptive Feature Fusion Network (CTAFFNet) for object detection. First, we design a Local–Global Feature Fusion unit known as the Convolutional Transformation Adaptive Fusion Kernel (CTAFFK), which is integrated into CTAFFNet. The CTAFFK kernel utilizes two branches, namely CNN and Transformer, to extract local and global features from the image, and adaptively fuses the features from both branches. Subsequently, we develop an adaptive feature fusion strategy that combines local high-frequency and global low-frequency features to obtain comprehensive feature information. Finally, CTAFFNet employs an encoder–decoder structure to facilitate the flow of fused local–global information between different stages, ensuring the model’s generalization capabilities. Results from the experiment conducted on the large and challenging KITTI dataset demonstrate the effectiveness and efficiency of the proposed network. Compared with other mainstream networks, it achieves an average precision of 91.17%, particularly excelling in the detection of small objects at longer distances with a remarkable 70.18% accuracy, while also providing real-time detection capabilities.

Lithium-ion batteries (LIBs), valued for their high energy density, long lifespan, and low environmental impact, are widely used in electric vehicles (EVs) and energy storage. However, increased energy density has exacerbated thermal runaway (TR) issues, hindering large-scale applications. This paper systematically analyzes the mechanisms of TR and strategies for early warning and prevention to enhance battery safety. It begins by detailing TR mechanisms and their triggers, then reviews various TR early warning technologies, fire prevention methods, and the effectiveness and mechanisms of novel extinguishing agents such as hydrogels, perfluorohexanone, liquid nitrogen (LN), dry powder, and aqueous vermiculite dispersion (AVD). The study also explores advancements in new fire-retardant coatings for batteries. Finally, it summarizes current challenges and forecasts future research directions in battery technology. This review offers readers a clear, systematic overview of TR mechanisms, warning systems, and prevention technologies, providing comprehensive insights into TR management.

In the wave of research on autonomous driving, 3D object detection from the Bird’s Eye View (BEV) perspective has emerged as a pivotal area of focus. The essence of this challenge is the effective fusion of camera and LiDAR data into the BEV. Current approaches predominantly train and predict within the front view and Cartesian coordinate system, often overlooking the inherent structural and operational differences between cameras and LiDAR sensors. This paper introduces CL-FusionBEV, an innovative 3D object detection methodology tailored for sensor data fusion in the BEV perspective. Our approach initiates with a view transformation, facilitated by an implicit learning module that transitions the camera’s perspective to the BEV space, thereby aligning the prediction module. Subsequently, to achieve modal fusion within the BEV framework, we employ voxelization to convert the LiDAR point cloud into BEV space, thereby generating LiDAR BEV spatial features. Moreover, to integrate the BEV spatial features from both camera and LiDAR, we have developed a multi-modal cross-attention mechanism and an implicit multi-modal fusion network, designed to enhance the synergy and application of dual-modal data. To counteract potential deficiencies in global reasoning and feature interaction arising from multi-modal cross-attention, we propose a BEV self-attention mechanism that facilitates comprehensive global feature operations. Our methodology has undergone rigorous evaluation on a substantial dataset within the autonomous driving domain, the nuScenes dataset. The outcomes demonstrate that our method achieves a mean Average Precision (mAP) of 73.3% and a nuScenes Detection Score (NDS) of 75.5%, particularly excelling in the detection of cars and pedestrians with high accuracies of 89% and 90.7%, respectively. Additionally, CL-FusionBEV exhibits superior performance in identifying occluded and distant objects, surpassing existing comparative methods.

Vehicle classification technology is one of the foundations in the field of automatic driving. With the development of deep learning technology, visual transformer structures based on attention mechanisms can represent global information quickly and effectively. However, due to direct image segmentation, local feature details and information will be lost. To solve this problem, we propose an improved vision transformer vehicle classification network (IND-ViT). Specifically, we first design a CNN-In D branch module to extract local features before image segmentation to make up for the loss of detail information in the vision transformer. Then, in order to solve the problem of misdetection caused by the large similarity of some vehicles, we propose a sparse attention module, which can screen out the discernible regions in the image and further improve the detailed feature representation ability of the model. Finally, this paper uses the contrast loss function to further increase the intra-class consistency and inter-class difference of classification features and improve the accuracy of vehicle classification recognition. Experimental results show that the accuracy of the proposed model on the datasets of vehicle classification BIT-Vehicles, CIFAR-10, Oxford Flower-102, and Caltech-101 is higher than that of the original vision transformer model. Respectively, it increased by 1.3%, 1.21%, 7.54%, and 3.60%; at the same time, it also met a certain real-time requirement to achieve a balance of accuracy and real time.

The behavior decision control of autonomous vehicles is a critical aspect of advancing autonomous driving technology. However, current behavior decision algorithms based on deep reinforcement learning still face several challenges, such as insufficient safety and sparse reward mechanisms. To solve these problems, this paper proposes a dueling double deep Q-network based on dual-priority experience replay—DPD3QN. Initially, the dueling network is integrated with the double deep Q-network, and the original network’s output layer is restructured to enhance the precision of action value estimation. Subsequently, dual-priority experience replay is incorporated to facilitate the model’s ability to swiftly recognize and leverage critical experiences. Ultimately, the training and evaluation are conducted on the OpenAI Gym simulation platform. The test results show that DPD3QN helps to improve the convergence speed of driverless vehicle behavior decision-making. Compared with the currently popular DQN and DDQN algorithms, this algorithm achieves higher success rates in challenging scenarios. Test scenario I increases by 11.8 and 25.8 percentage points, respectively, while the success rates in test scenarios I and II rise by 8.8 and 22.2 percentage points, respectively, indicating a more secure and efficient autonomous driving decision-making capability.