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Automation is increasingly at the forefront of transportation research, with the potential to bring fully autonomous vehicles to our roads in the coming years. This comprehensive survey provides a holistic look at the essential components and cutting-edge technologies that are driving the development and implementation of autonomous driving. It starts by evaluating two critical system architectures that are fundamental to the operation of autonomous vehicles: the layered and end-to-end structures. It then examines the critical areas of scene perception and localization, emphasizing the importance of sensor technologies. These technologies are vital for tasks such as object detection and semantic segmentation, which allow vehicles to understand and navigate their environment. A special focus is given to the complex topic of object detection, along with suggestions for how it can be enhanced. The survey then proceeds to provide detailed discussions on path planning, trajectory prediction, and decision-making processes. These elements are crucial for the smooth navigation of autonomous vehicles, and the survey highlights the role of artificial intelligence (AI) and machine learning in these processes. Overall, the survey presents the rapid progress in the field of autonomous driving, offering a comprehensive assessment of the technologies and innovations that are essential for moving toward a safe and efficient autonomous future. [ABSTRACT FROM AUTHOR]

Self-supervised visual representation learning focuses on capturing comprehensive features via exploiting the unlabeled datasets. However, existing contrastive learning based SSL frameworks are subjected to higher computational consumption and unsatisfactory performance. To handle these issues, we present a novel single-branch SSL method that incorporates an adaptive feature selection and activation module and a progressive cascade dimensionality reduction module, called APNet. Specifically, our method first fully exploits the unlabeled datasets and extracts intra- and inter-image information via introducing montage image. In addition, a novel adaptive feature selection and activation module is designed to generate the most comprehensive features. Besides, a progressive cascade dimensionality reduction module is proposed to capture the most representative features from latent vectors through cascade dimensionality increasing–decreasing operations. Extensive experiments have demonstrated the robustness and effectiveness of APNet. Specifically, APNet exceeds MoCo-v3 by 3.1% on the ImageNet-100 dataset, and consumes only half of the calculation. Code is available at https://github.com/AI-TYQ/APNet. • We embed feature selection and dimensionality reduction modules into SSL framework. • We design an attention module that selects and activates representative features. • We introduce a dimensionality reduction module to retain discriminative features. • We demonstrate the advantages of the above contributions through experiments. [Display omitted] [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a common arrhythmia, and its incidence increases with age. Many methods have been developed to identify AF, including both the hand-picked features by experts and the recent emerging artificial intelligent (AI) methods. As the traditional hand-picked features have almost reached the boundary of their capability, the AI methods have shown their great potentials to achieve high accuracy for the AF identification. However, some common AI methods, especially deep learning methods, do not provide good properties of interpretability, making it difficult to explore the internal relationship between input and prediction results. In addition, most of the reported methods are only for the intra-patient test of AF and Non-AF. In this study, we try to develop an AF detector based on a twin-attentional convolutional long short-term memory neural network (TAC-LSTM), which can not only generate results with high accuracy but also enable a human-friendly function to provide the possible explanations of the automated extracted features by AI. TAC-LSTM was applied to extract multi-domain features of ECG signals for AF detection and to mine the influence of different input segments on the final prediction results. Finally, the proposed method is validated on the MIT-BIH Atrial Fibrillation Database (AFDB) with intra-patient test and inter-patient test and the results also have shown that multi-domain features extracted by TAC-LSTM can provide more useful information. Collectively, TAC-LSTM can be used for clinicians as an auxiliary diagnostic tool. • A novel deep neural network with multi-domain input layers is proposed. • CNN and LSTM are employed to extract short-term and long-term dependence features. • In AF detection, multi-domain information is characterized by complementary. • Fusing signal time and time-frequency domain information improves model performance. • Our method mines the relationship between the input segment and prediction results. [ABSTRACT FROM AUTHOR]