Search

Adaptive cruise control (ACC) enables efficient, safe, and intelligent vehicle control by autonomously adjusting speed and ensuring a safe following distance from the vehicle in front. This paper proposes a novel adaptive cruise system, namely the Safety-First Reinforcement Learning Adaptive Cruise Control (SFRL-ACC). This system aims to leverage the model-free nature and high real-time inference efficiency of Deep Reinforcement Learning (DRL) to overcome the challenges of modeling difficulties and lower computational efficiency faced by current optimization control-based ACC methods while simultaneously maintaining safety advantages and optimizing ride comfort. Firstly, we transform the ACC problem into a safe DRL formulation Constrained Markov Decision Process (CMDP) by carefully designing state, action, reward, and cost functions. Subsequently, we propose the Projected Constrained Policy Optimization (PCPO)-based ACC Algorithm SFRL-ACC, which is specifically tailored to solve the CMDP problem. PCPO incorporates safety constraints that further restrict the trust region formed by the Kullback–Leibler (KL) divergence, facilitating DRL policy updates that maximize performance while keeping safety costs within their limit bounds. Finally, we train an SFRL-ACC policy and compare its computation time, traffic efficiency, ride comfort, and safety with state-of-the-art MPC-based ACC control methods. The experimental results prove the superiority of the proposed method in the aforementioned performance aspects. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks. However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. In consideration of the above problems, in this paper, we propose a novel fully convolutional auto-encoder based on dual clustering and latent feature adversarial consistency (FCAE-DCAC) for HAD, which is carried out with self-supervised learning-based processing. Firstly, density-based spatial clustering of applications with a noise algorithm and connected component analysis are utilized for successive spectral and spatial clustering to obtain more precise prior background and anomaly information, which facilitates the separation between background and anomaly samples during the training of our method. Subsequently, a novel fully convolutional auto-encoder (FCAE) integrated with a spatial–spectral joint attention (SSJA) mechanism is proposed to enhance the utilization of spatial information and augment feature expression. In addition, a latent feature adversarial consistency network with the ability to learn actual background distribution in hyperspectral imagery is proposed to achieve pure background reconstruction. Finally, a triplet loss is introduced to enhance the separability between background and anomaly, and the reconstruction residual serves as the anomaly detection result. We evaluate the proposed method based on seven groups of real-world hyperspectral datasets, and the experimental results confirm the effectiveness and superior performance of the proposed method versus nine state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Fractal integro-differential equations (IDEs) can describe the effect of local microstructure on a complex physical problem, however, the traditional numerical methods are not suitable for solving the new-born models with the fractal integral and fractal derivative. Here we show that deep learning can be used to solve the bottleneck. By the two-scale transformation, the fractal IDE is first approximately converted to its traditional integro-differential partner, which is further converted to a differential equation system by introducing an auxiliary variable to remove the integral operation. Moreover, a flexible adaptive technology is adopted to deal with the loss weights of a deep learning neural network. A fractal Volterra IDE is used to show the effectiveness and simplicity of this new physics-informed deep AI simulation model. All results indicate the AI simulation model has good robustness and convergence, and the fractal Volterra IDE might explore the different properties of viscoelasticity for a porous medium. [ABSTRACT FROM AUTHOR]

Finer resolution is one of the development trends in ocean surface waves simulation and forecasting. However, high-resolution numerical models for ocean surface waves have led to an enormous increase in computational complexity, posing a challenge with respect to balancing computational efficiency and timeliness. To meet the demand for refined ocean surface waves simulation/forecasting and to address the computational efficiency challenge of high-resolution ocean surface waves models, we propose a downscaling model called the Global location-Specific transformation Downscaling Network (GSDNet) based on the non-autoregressive fusion network (NAFNet). By incorporating global location-specific transformation and introducing a land–sea distribution indicator, GSDNet can quickly and accurately map low-resolution significant wave heights to high-resolution grids. The results show that, compared with traditional interpolation methods such as the bilinear, inverse distance weight interpolation (IDW), and bicubic methods, the GSDNet model can reduce the global mean absolute error (MAE) by >77%. Compared with those of FourCastNet (FCN), the Koopman neural operator (KNO), the original NAFNet, and residual networks in deep learning from empirical downscaling methods (DL4DS_ResNet), the MAE decreases by >21%. Furthermore, the GSDNet model outperforms the other downscaling methods at the coastal boundary and for identifying the maximum significant wave height. In this work, we provide an effective solution for balancing computational efficiency and timeliness, which is important for improving the accuracy and reliability of ocean surface waves simulation/forecasting. • GSDNet demonstrates excellent downscaling ability for global wave heights. • GS is designed to capture fine features in margin regions. • GSDNet has a good generalization to apply in different resolutions via fine-tuning. [ABSTRACT FROM AUTHOR]

As an enhanced version of standard CAN, the Controller Area Network with Flexible Data (CAN-FD) rate is vulnerable to attacks due to its lack of information security measures. However, although anomaly detection is an effective method to prevent attacks, the accuracy of detection needs further improvement. In this paper, we propose a novel intrusion detection model for the CAN-FD bus, comprising two sub-models: Anomaly Data Detection Model (ADDM) for spotting anomalies and Anomaly Classification Detection Model (ACDM) for identifying and classifying anomaly types. ADDM employs Long Short-Term Memory (LSTM) layers to capture the long-range dependencies and temporal patterns within CAN-FD frame data, thus identifying frames that deviate from established norms. ACDM is enhanced with the attention mechanism that weights LSTM outputs, further improving the identification of sequence-based relationships and facilitating multi-attack classification. The method is evaluated on two datasets: a real-vehicle dataset including frames designed by us based on known attack patterns, and the CAN-FD Intrusion Dataset, developed by the Hacking and Countermeasure Research Lab. Our method offers broader applicability and more refined classification in anomaly detection. Compared with existing advanced LSTM-based and CNN-LSTM-based methods, our method exhibits superior performance in detection, achieving an improvement in accuracy of 1.44% and 1.01%, respectively. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) plays a critical role in the prediction, planning, and control of autonomous vehicle. The original motion control methods are increasing in accuracy, but their control style markedly differs from that of human drivers. This not only degrades the passenger's ride experience but also brings unfamiliarity to other drivers, leading to safety hazards. Moreover, some of the methods are a direct application of AI techniques without any specific enhancements tailored for vehicle control. This both fails to fully utilize the potential of AI and reduces interpretability. To address these issues, in this paper, deep learning methods are integrated with driver control mechanisms to propose a human-like neural network for vehicle longitudinal dynamics estimation and control (HNN). First, the process of driver estimation and control of vehicle dynamics is proposed and described in mathematical language. Subsequently, the HNN composed of three sub-networks is presented. The three sub-networks correspond to the three sub-processes of the driver's mechanism, which makes the proposed HNN method more applicable to vehicle dynamics control and more interpretable in specific engineering domains. The effectiveness of the HNN method is verified on a real-world dataset. The results demonstrate that the HNN not only enhances vehicle human-like control but also surpasses baselines in terms of control style consistency and convergence rate. • The method is a deep integration of human control mechanisms and artificial intelligence in a specific engineering domain. • The proposed deep learning method has a unique structure derived from the driver control mechanism.The structure of the method makes it more applicable for vehicle motion control with active rule-based interpretability. • The tolerance control pattern is proposed and incorporated into the mechanism of driver control vehicle dynamics. • Compared to the baselines, the method proves to be more suitable for the engineering field of vehicle human-like control. [ABSTRACT FROM AUTHOR]