The geometry of road surfaces plays a critical role in the performance of autonomous driving systems. Consequently, achieving accurate and efficient road surface reconstruction (RSR) is of paramount importance. However, due to the inherent effects of perspective projection, distant regions often exhibit geometric distortions and a long-tailed distribution, which pose significant challenges to existing reconstruction methods. To address these issues, we propose a novel framework, termed Direction-aware Pseudo-Stereo Road Reconstruction Network (DPS-Net), which incorporates two lightweight and plug-and-play modules: Direction-Aware Feature Enhancement (DFE) module and Pseudo-Stereo Fusion (PSF) module. The DFE module is designed to enhance the perception of sparse and geometry-invariant features by integrating directional context, while the PSF module captures global dependencies across spatial and channel dimensions through pseudo-stereo fusion. Both modules are constructed with an emphasis on maintaining low computational complexity. We conducted extensive experiments on the public RSRD dataset to evaluate the effectiveness and superiority of our proposed method. The code is available at https://github.com/yidanyi/DPS-Net.

The autoencoder (AE) is an efficient feature extraction tool that learns latent representations from raw data by minimizing the reconstruction loss. Building upon the AE architecture, deep clustering models are designed to jointly optimize the deep neural network and perform unsupervised clustering. However, existing methods directly impose the clustering objective on the latent features produced by the AE network, thereby neglecting the potential conflict between data clustering and data representation. Specifically, data clustering aims to enhance data aggregation, whereas data representation focuses on ensuring that latent features faithfully reflect the manifold structure of the raw data. To address this issue, this article proposes an innovative expanded deep embedding clustering (E-DEC) model, in which the AE network is employed to seek better latent representations, and a novel residual expansion module (REM) is integrated to construct an expanded feature space that better serves clustering tasks. Furthermore, adversarial learning between the soft cluster assignments and a prior one-hot distribution is adopted in lieu of the conventional Kullback–Leibler (KL) divergence, so as to enhance the discrimination of different clusters and avoid the degeneracy problem. Finally, an entropy regularization technique is incorporated to adaptively refine the affinity graph throughout the clustering process, thereby reducing the sensitivity of clustering performance to the initial affinity graph. Extensive experiments on real-world benchmark datasets demonstrate the superiority of the proposed model over state-of-the-art deep clustering methods.

Reinforcement learning (RL) has gained significant attention due to its end-to-end learning capabilities and model-free nature. In the realm of vehicle engineering, active suspensions are crucial for enhancing both comfort and safety. The Deep Deterministic Policy Gradient (DDPG) algorithm, characterized by its stability and efficiency, offers valuable guidance for addressing the nonlinear issues in suspension systems. However, its learning efficiency may be compromised when dealing with high-dimensional state or action spaces. Therefore, this research primarily focuses on improving the DDPG algorithm by implementing enhancements by improved reward modules and integrating Long Short-Term Memory (LSTM), in order to maximize its control performance. By conducting five sets of experiment, the results demonstrate that the DDPG algorithm, after being integrated with various methods, exhibits superior control performance.

Missing traffic data caused by sensor failures or communication errors significantly hinders the efficiency of downstream tasks in Intelligent Transportation Systems (ITS), such as the critical functions of traffic monitoring and decision-making. Since missing data contains important information, it is essential to extract dynamic spatial-temporal correlations in traffic processes by incorporating these missing features. Motivated by these concerns, a novel Dynamic Spatial-Temporal Imputation Network with Missing Features (DSTMIN) is proposed to accurately impute traffic data. DSTMIN comprises an embedding layer, a Mask Attention module (MA), and a Fusion Graph Convolution module (FGC). Specifically, an embedding layer is designed to accurately represent the distribution of missing data, thereby capturing both temporal features and missing features. Furthermore, in order to effectively capture the temporal correlations, MA integrates the missing features to emphasize the significance of observed data and reduce the adverse effects caused using incomplete data. To capture spatial correlations, FGC constructs the spatial graphs and model dynamic spatial correlations from traffic subsequences and the missing-data graph in the presence of missing features. The proposed DSTMIN is adequately evaluated to demonstrate its superior performance on two datasets, which achieves a remarkable 20% reduction in imputation error compared to state-of-the-art methods.

Distributed clustering based on the Gaussian mixture model (GMM) has exhibited excellent clustering capabilities in peer-to-peer (P2P) networks. However, more iterative numbers and communication overhead are required to achieve the consensus in existing distributed GMM clustering algorithms. In addition, the truth that it cannot find a closed form for the update of parameters in GMM causes the imprecise clustering accuracy. To solve these issues, by utilizing the transfer learning technique, a general transfer distributed GMM clustering framework is exploited to promote the clustering performance and accelerate the clustering convergence. In this work, each node is treated as both the source domain and the target domain, and these nodes can learn from each other to complete the clustering task in distributed P2P networks. Based on this framework, the transfer distributed expectation-maximization algorithm with the fixed learning rate is first presented for data clustering. Then, an improved version is designed to obtain the stable clustering accuracy, in which an adaptive transfer learning strategy is adopted to adjust the learning rate automatically instead of a fixed value. To demonstrate the extensibility of the proposed framework, a representative GMM clustering method, the entropy-type classification maximum-likelihood algorithm, is further extended to the transfer distributed counterpart. Experimental results verify the effectiveness of the presented algorithms in contrast with the existing GMM clustering approaches.

Collaborative multiview clustering methods can efficiently realize the view fusion by exploring complementary and consistent information among multiple views. However, these studies ignore all the differences between multiple views in fusion. In fact, in the multiview clustering, the data are diverse from view to view. The larger the difference between any two views is, the more the fusion of these views is required. Moreover, a global tradeoff parameter is generally adopted to restrain the penalty related to the disagreement of all views, which is often defined empirically. Inspired by the idea of transfer learning, a series of novel collaborative multiview clustering algorithms are proposed to tackle these challenges. In the most basic one, each view performs clustering independently and learns from others to improve its own clustering performance, in which a global learning factor is defined to control the interaction between multiple views. The fuzzy memberships are regarded as the important knowledge to provide guidance between views, and the consensus constraint is defined to ensure the consistent partitions of all views. In addition, the local adaptive learning factors between any two views instead of a global fixed one are adopted in an improved version to emphasize the difference between views, and the adjustment strategy for the learning factor is further designed to guarantee the stability of multiview clustering without the influence of initial values. Finally, to identify the significance of different views to the clustering, the extended versions are excavated with the assignment of view weights and the maximum entropy regularization technique is employed to optimize the weights. Experiments on various real-world multiview datasets verify the superiority of the presented approaches.

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What’s more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexity.

In recent years, kernel-based fuzzy clustering has attracted significant attention, primarily benefiting from the outstanding performance of capturing the potential non-linear structure in data clustering. However, many existing kernel clustering methods are not available for large datasets due to computational costs. To overcome this limitation, the low-rank random feature map is utilized to approximate the kernel space. Nevertheless, this kind of feature approximation method ignores the graph structure information hidden in the data and does not take the correlations between data samples in the clustering into account. Thus, we present a new kernel fuzzy clustering based on Quasi-Monte Carlo feature map with neighbor affinity constraint (Na_QMC_KFC). In this scheme, the Quasi-Monte Carlo method is adopted to approximate the Gaussian kernel function so as to reduce the computational costs. Meanwhile, the neighbor affinity constraint is designed to maintain the graph structure information of the data and further facilitate the consistency of the membership degrees and the raw data. What’s more, the Alternating Direction Method of Multipliers method is utilized to optimize the problem with respect to the neighbor affinity lasso. The experiments on several non-linear and real-world datasets exhibits the efficiency of the presented algorithm.

The integration of reinforcement learning with fuzzy logic can be effective in compensating the external disturbance and complex dynamic while designing the control strategy for vehicle suspension. The main contribution of this paper is that a learning-based adaptive vibration control strategy is proposed for semi-active suspension system, which combines the fuzzy logic with the reward function of reinforcement learning to improve the robustness and feasibility of the vibration control strategy. What’s more, an improved proximal policy optimization algorithm combined with fuzzy logic is proposed for realizing the trial-and-error reinforcement learning. Specially, the reward function with fuzzy logic is formulated to meet the requirements of suspension performance under different road conditions, in which the fuzzy logic is designed to fuzzily the process the collected road information, real-time update the weight matrix coefficients, and adjust the optimization objectives adaptively. Finally, numerical simulation results are given to prove the effectiveness of the proposed vibration control strategy.

The reinforcement learning (RL) with deep neural network, as a data-driven approach, is promising for adaptive traffic signal control (ATSC) in traffic scenarios. The majority of the existing studies focus on designing efficient agents and policy optimization for ATSC, but neglect to observe more detailed states of the environment. In this paper, an adaptive traffic signal control strategy, named as A2C RTQL, is proposed for scheduling the traffic signal in an intersection, by combining the real-time lane-based queue lengths with deep RL agent. First, the Lighthill-Whitham-Richards (LWR) shockwave theory is employed for obtaining the real-time queue lengths in each lane. After that, by defining the obtained queue lengths as the inputs, A2C RTQL strategy is designed for traffic signal control based on the advanced actor-critic (A2C) agent, where the lanes are divided into multiple parallel environments based on the phases of traffic signal. Simulation results demonstrate the optimality and efficiency of the proposed strategy compared with other methods in SUMO under simulated peak-hour traffic dynamics.

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.

In this article, the fault-tolerant control (FTC) problem of vehicle active suspension is concerned in the discrete-time domain, in which the road disturbances and faults in actuator and measurement are considered. The main contribution consists of proposing an active physically realizable fault-tolerant controller based on a reduced-order observer, which makes up an optimal vibration control component and an event-triggered FTC component. More specifically, by discussing a discrete vehicle active suspension subject to road disturbances generated from the output of a designed exosystem, the optimal vibration control component is derived from maximum principle to offset the inevitable vibrations. Meanwhile, based on the real-time system output of vehicle suspension rather than residual error, a reduced-order observer is proposed to cover the physically unrealizable problem for the designed optimal vibration control component. After that, an event-triggered FTC component and an event-triggered restructured system output are designed to compensate the faults in actuator and measurement, respectively. Finally, extensive experiments are conduced to the control performance of vehicle active suspension under the proposed controller, and confirm its effectiveness and superiority over other control schemes.

Combining the computational intelligence with dynamic responses of vehicle suspension for estimating the road profiles provides effective tool for designing various control strategies. In this paper, a FNT-based road profile classification method is proposed based on the dynamic responses of a quarter semi-active suspension under PID controller and road disturbances generated from power spectral density under the ISO 8608 standard. More specially, a data preprocessing method is designed to reduce the impact of vehicle velocity on dynamic response and determine the appropriate size of the spatial domain for data collection. After that, FNT is employed as the basic model to screen these extracted features for road profile classification with low computational consumption of road evaluation. From the numerical simulation results, the classification accuracy is 98.41% under the proposed road profile classification with six input variables.

For image segmentation, fuzzy clustering methods with single spatial information cannot ensure robustness to the image corrupted by different noises. In this paper, to figure out this problem, we propose a multiple spatial information weighted fuzzy clustering method, in which the original pixel intensity and its two spatial information, the mean and median of neighbors within a local window, are combined with different weights to obtain precise segmentation results of noise images. And the entropy-regularized method is employed to optimize the weight of each term to handle the images with different noise. What's more, the kernelization of the proposed method is presented to relief the impact of outliers. It is worth noting that our methods can be further extended by combining with other spatial information. Experiments on synthetic images and natural images show the superiority and efficiency of the proposed methods.

The centralized longitudinal control problem for Cooperative Adaptive Cruise Control (CACC) systems is discussed in this paper, in which the imperfect wireless communication surroundings and actuator dynamics are taken into consideration. From the large-scale system standpoint, the centralized longitudinal control problem for platoon vehicles equipped with CACC functionality is formulated as minimizing a quadratic performance index under the constrains of a large-scale discrete-time system with actuator delay and system output delay, in which the leader vehicle is set as the control center. After that, benefiting from a designed delay-free transformed vector, a delay-free two-point-boundary-value problem is derived from the equivalent reconstruction forms for original system delay model and performance index. Thus the centralized longitudinal controller is obtained by solving a Riccati equation. Finally, simulation results demonstrate that the ego vehicle can reasonable response the accelerating or decelerating behaviors of the preceding vehicles under the proposed controller, thereby the desired CACC control performance is satisfied, and the wireless communication delay and actuator delay are compensated effectively.

Spectral clustering is one of the most effective methods of data mining, in which the adjacency matrix is constructed by using the similarity matrix. In this paper, to extend spectral clustering method for uncertain data clustering, we propose a new spectral clustering method based on JS-divergence. In the proposed method, the JS-divergence is used to construct the adjacency matrix in the spectral clustering, which is more suitable to calculate the similarity between uncertain data objects as a symmetrical measurement compared to the KL-divergence.

This paper considers the sliding mode control (SMC) for a class of state delayed systems subject to persistent disturbances. First, a disturbance compensator is proposed to eliminate the influence from persistent disturbances, and the stability of control system is discussed. Then, the control problem is transformed into sliding mode control problem for state delayed system without expression of disturbances. The reduced-order sliding mode surface function is proposed based on the Lyapunov-Functional and the designed switching function. Furthermore, sliding mode control law is obtained. Finally, the simulation results demonstrate that the proposed control law can guarantee the stability of state delayed systems.

Uncertain data clustering is an essential task in the research of data mining. Lots of traditional clustering methods are extended with new similarity measurements to tackle this issue. Different from certain data clustering, uncertain data clustering focus more on the evaluation of distribution similarity between uncertain data objects. In this paper, based on the KL-divergence and the JS-divergence, we propose a novel K-medoids method for clustering uncertain data, named UK-medoids. Good performance of the proposed algorithm is shown in experiments on synthetic datasets.

This paper presents a longitudinal tracking control law for Cooperative Adaptive Cruise Control (CACC) systems in a platoon that can comprehensively enable tracking capability of various spacing policies, designed expected velocity, and designed expected acceleration. Taking into account heterogeneous traffic, i.e., a platoon of vehicles with possibly different characteristics, the longitudinal control problem is formulated as an output tracking control problem with a quadratic function so that the contradictions among the different tracking requirements are realized, which include inter-vehicle spacing, velocity and acceleration. Then, the decentralized longitudinal tracking control law is proposed by using a limited communication structure and maximum principle (in this case, a wireless communication link with the nearest preceding vehicle and designed platoon leader only), in which the feedback items are composed of the states of host vehicles, and additional information of the nearest preceding vehicle and designed platoon leader are used as feed forward items. In addition, the concepts of "expected velocity" and "expected acceleration" are introduced to design the desired velocity and acceleration, realize additional objectives, and improve the predictive abilities. Numerous simulation results show that the proposed tracking controller provides a reliable tool for a systematic and efficient design of a platoon controller within CACC systems.