Most current multi-view clustering methods necessitate that a sample's features be view-aligned or at least partially aligned across different views. Regrettably, real-world applications often fail to meet this requirement due to spatial, temporal, or spatiotemporal mismatches, resulting in the view-unaligned issue. To tackle this issue, we conceptualize the view-unaligned problem and demonstrate that it can be transformed into a view-aligned problem through reordering. Building on this concept, we introduce an innovative reorder matrix that realigns view-unaligned features. Utilizing these realigned features, we develop a sophisticated and efficient approach called Reordered$k$-means (RKM), which merges NMF with$k$-means. Unlike traditional$k$-means, our method converts the binary challenge into an$\ell _{0}$problem, confirming the merit of this advancement. Furthermore, RKM's efficacy is affirmed on benchmarks, indicating substantial enhancements in handling the view-unaligned issue and maintaining competitive results with view-aligned problems.

Real-time intelligent navigation services can be found everywhere in our daily lives. However, there are many factors that need to be considered when implementing intelligent navigation, such as the security and stability of intelligent navigation services. Most existing state-of-the-art approaches use pseudonymous, public-key fully homomorphic encryption (FHE) to protect location privacy. However, the collected real-time data is not processed in the existing schemes, and malicious data can affect the real-time navigation results. In this paper, we have proposed a privacy-preserving navigation scheme in VANETs that can detect malicious data by using Boolean secret sharing at low cost to filter malicious data and prevent resource abuse. It is also combined with the DTPKC cryptosystem to protect the user’s private information to provide reliable security for the system and ensure that the private information remains hidden from untrustworthy third parties. Meanwhile, the Chinese remainder theorem (CRT) is used in the key generation phase to negotiate the key value between the two parties, and the symmetric key is derived from the key value for sending the navigation results to improve the acquisition efficiency and increase the user experience. We demonstrate the security and practicality of our scheme through SUMO simulation experiments.

Multi-view representation learning, aimed at uncovering the inherent structure within multi-view data, has developed rapidly in recent years. In practice, due to temporal and spatial desynchronization, it is common that only part of the data is aligned between views, which leads to thePartial View Alignment(PVA) problem. To address the challenge of representation learning on partially view-aligned multi-view data, we propose a new cross-view graph contrastive learning network, which integrates multi-view information to align data and learn latent representations. First, view-specific autoencoders are used to construct an end-to-end multi-view representation learning framework for learning specific view representations. Furthermore, to achieve cluster-level alignment, we introduce a cross-view graph contrastive learning module to guide the learning of discriminative representations. Compared to the existing methods, the proposed cluster-level alignment method successfully extends the view alignment to more than two views. Meanwhile, the results of clustering and classification experiments on several popular multi-view datasets can also illustrate the effectiveness and superiority of the proposed method.

Autonomous driving requires efficient and safe decision making and motion planning in dynamic and uncertain environments. Future movement of surrounding vehicles is often difficult to represent. Besides, most existing studies consider decision making and planning/control separately. Both them may lead to the oscillation and unsafe for autonomous driving. This paper proposes an integrated framework of decision making and motion planning with oscillation-free capability. The proposed approach overcomes the shortcomings of autonomous driving for lane change/keeping maneuvers and is able to: i) make oscillation-free behavior decisions given biased prediction; ii) cut through in the traffic efficiently and safely when being in squeezed; iii) accelerate computation efficiency by building a state transfer model based on prediction uncertainty; iv) reduce the dissonance between decision-making and motion planning. A belief decision planner is designed with the uncertainty of the prediction trajectories. Lateral and longitudinal drivable corridors including the reference state and the related boundary constraints are built, which provide better suited information for planning to solve the optimal motion sequence more quickly and stably, and improve its consistency with decision module. Finally, the problem is formulated as an optimal control problem considering the vehicle dynamics and some soft constraints and the motion trajectory is solved by OSQP. Simulation and experimental tests are implemented to evaluate the feasibility and effectiveness of the proposed approach. Test results show that the integrated approach can make proper, safe and continuous decision and planning for autonomous vehicles and the calculation time is very low.

Recently, there has been considerable research interest in graph clustering aimed at data partition using graph information. However, one limitation of most graph-based methods is that they assume that the graph structure to operate is reliable. However, there are inevitably some edges in the graph that are not conducive to graph clustering, which we call spurious edges. This brief is the first attempt to employ the graph pooling technique for node clustering to the best of our knowledge. In this brief, we propose a novel dual graph embedding network (DGEN), which is designed as a two-step graph encoder connected by a graph pooling layer to learn the graph embedding. In DGEN, we assume that if a node and its nearest neighboring node are close to the same clustering center, this node is informative, and this edge can be considered as a cluster-friendly edge. Based on this assumption, the neighbor cluster pooling (NCPool) is devised to select the most informative subset of nodes and the corresponding edges based on the distance of nodes and their nearest neighbors to the cluster centers. This can effectively alleviate the impact of the spurious edges on the clustering. Finally, to obtain the clustering assignment of all nodes, a classifier is trained using the clustering results of the selected nodes. Experiments on five benchmark graph datasets demonstrate the superiority of the proposed method over state-of-the-art algorithms.

Due to the development of facial manipulation technologies, the generated deepfake videos cause a severe trust crisis in society. Existing methods prove that effective extraction of the artifacts introduced during the forgery process is essential for deepfake detection. However, since the features extracted by supervised binary classification contain a lot of artifact-irrelevant information, existing algorithms suffer severe performance degradation in the case of the mismatch between training and testing datasets. To overcome this issue, we propose an Artifacts-Disentangled Adversarial Learning (ADAL) framework to achieve accurate deepfake detection by disentangling the artifacts from irrelevant information. Furthermore, the proposed algorithm provides visual evidence by effectively estimating artifacts. Specifically, Multi-scale Feature Separator (MFS) in the disentanglement generator is designed to precisely transmit the artifact features and optimize the connection between the encoder and decoder. In addition, we design an Artifacts Cycle Consistency Loss (ACCL) which uses the disentangled artifacts to construct new samples and enables pixel-level supervised training for the generator to estimate more accurate artifacts. The symmetric discriminators are paralleled to differentiate the constructed samples from the original images in both fake and real domains, making the adversarial training process more stable. Extensive experiments on existing benchmarks demonstrate that the proposed method outperforms the state-of-the-art approaches.

In this paper, we consider the problem of multi-view clustering on incomplete views. Compared with complete multi-view clustering, the view-missing problem increases the difficulty of learning common representations from different views. To address the challenge, we propose a novel incomplete multi-view clustering framework, which incorporates cross-view relation transfer and multi-view fusion learning. Specifically, based on the consistency existing in multi-view data, we devise a cross-view relation transfer-based completion module, which transfers known similar inter-instance relationships to the missing view and infers the missing data via graph networks based on the transferred relationship graph. Then the view-specific encoders are designed to extract the recovered multi-view data, and an attention-based fusion layer is introduced to obtain the common representation. Moreover, to reduce the impact of the error caused by the inconsistency between views and obtain a better clustering structure, a joint clustering layer is introduced to optimize recovery and clustering simultaneously. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method.

Latent low-rank representation (LatLRR) is a critical self-representation technique that improves low-rank representation (LRR) by using observed and unobserved samples. It can simultaneously learn the low-dimensional structure embedded in the data space and capture the salient features. However, LatLRR ignores the local geometry structure and can be affected by the noise and redundancy in the original data space. To solve the above problems, we propose a latent LRR with weighted distance penalty (LLRRWD) for clustering in this article. First, a weighted distance is proposed to enhance the original Euclidean distance by enlarging the distance among the unconnected samples, which can enhance the discriminitation of the distance among the samples. By leveraging on the weighted distance, a weighted distance penalty is introduced to the LatLRR model to enable the method to preserve both the local geometric information and global information, improving discrimination of the learned affinity matrix. Moreover, a weight matrix is imposed on the sparse error norm to reduce the effect of noise and redundancy. Experimental results based on several benchmark databases show the effectiveness of our method in clustering.

With the diversity of information acquisition, data is stored and transmitted in an increasing number of modalities. Nevertheless, it is not unusual for parts of the data to be lost in some views due to unavoidable acquisition, transmission or storage errors. In this paper, we propose an augmentation-free graph contrastive learning framework to solve the problem of partial multi-view clustering. Notably, we suppose that the representations of similar samples (i.e., belonging to the same cluster) should be similar. This is distinct from the general unsupervised contrastive learning that assumes an image and its augmentations share a similar representation. Specifically, relation graphs are constructed using the nearest neighbors to identify existing similar samples, then the constructed inter-instance relation graphs are transferred to the missing views to build graphs on the corresponding missing data. Subsequently, two main components, within-view graph contrastive learning and cross-view graph consistency learning, are devised to maximize the mutual information of different views within a cluster. The proposed approach elevates instance-level contrastive learning and missing data inference to the cluster-level, effectively mitigating the impact of individual missing data on clustering. Experiments on several challenging datasets demonstrate the superiority of our proposed methods.

Graph-based multi-view clustering aiming to obtain a partition of data across multiple views, has received considerable attention in recent years. Although great efforts have been made for graph-based multi-view clustering, it is still challenging to fuse characteristics from various views to learn a common representation for clustering. In this paper, we propose a novel Consistent Multiple Graph Embedding Clustering framework (CMGEC). Specifically, a multiple graph auto-encoder (M-GAE) is designed to flexibly encode the complementary information of multi-view data using a multi-graph attention fusion encoder. To guide the learned common representation maintaining the similarity of the neighboring characteristics in each view, a Multi-view Mutual Information Maximization module (MMIM) is introduced. Furthermore, a graph fusion network (GFN) is devised to explore the relationship among graphs from different views and provide a common consensus graph needed in M-GAE. By jointly training these models, the common representation can be obtained, which encodes more complementary information from multiple views and depicts data more comprehensively. Experiments on three types of multi-view datasets demonstrate CMGEC outperforms the state-of-the-art clustering methods.

Existing low-rank representation-based methods adopt a two-step framework, which must employ an extra clustering method to gain labels after representation learning. In this paper, a novel one-step representation-based method, i.e., One-step Low-Rank Representation (OLRR), is proposed to capture multi-subspace structures for clustering. OLRR integrates the low-rank representation model and clustering into a unified framework. Thus it can jointly learn the low-rank subspace structure embedded in the database and gain the clustering results. In particular, by approximating the representation matrix with two same clustering indicator matrices, OLRR can directly show the probability of samples belonging to each cluster. Further, a probability penalty is introduced to ensure that the samples with smaller distances are more inclined to be in the same cluster, thus enhancing the discrimination of the clustering indicator matrix and resulting in a more favorable clustering performance. Moreover, to enhance the robustness against noise, OLRR uses the probability to guide denoising and then performs representation learning and clustering in a recovered clean space. Extensive experiments well demonstrate the robustness and effectiveness of OLRR. Our code is publicly available at: https://github.com/fuzhiqiang1230/OLRR.

This paper presents a novel, simple yet robust self-representation method, i.e., Double Low-Rank Representation with Projection Distance penalty (DLRRPD) for clustering. With the learned optimal projected representations, DLRRPD is capable of obtaining an effective similarity graph to capture the multi-subspace structure. Besides the global low-rank constraint, the local geometrical structure is additionally exploited via a projection distance penalty in our DLRRPD, thus facilitating a more favorable graph. Moreover, to improve the robustness of DLRRPD to noises, we introduce a Laplacian rank constraint, which can further encourage the learned graph to be more discriminative for clustering tasks. Meanwhile, Frobenius norm (instead of the popularly used nuclear norm) is employed to enforce the graph to be more block-diagonal with lower complexity. Extensive experiments have been conducted on synthetic, real, and noisy data to show that the proposed method outperforms currently available alternatives by a margin of 1.0%~10.1%.

Motion planning and vehicle prediction play an important role for autonomous vehicle, which aims to guarantee the driving safety under cut-in scenarios. This paper presents a hybrid prediction model for computing the future trajectory of the surrounding vehicles and a dynamic speed planner based on model predictive control to avoid collisions. Firstly, the hybrid prediction model combines the physics-based model and behavior-based model through Mamdani fuzzy logic. The predicted physic trajectory is computed using the constant yaw rate and velocity vehicle model. In addition, the prediction of driving intention is accomplished by using information of the difference between current motion and driving lanes. Furthermore, the predicted behavior trajectory is selected from the candidate quintic polynomial trajectory cluster through the designed cost function. Then, Gaussian propagation is applied at the fusion trajectory to compute the uncertainty distribution. Secondly, the dynamic speed planner based on model predictive control provides the optimal control command for the collision avoidance maneuver, which considers the future trajectory distribution of surrounding vehicles. Finally, the effectiveness of the proposed method is verified through simulation in different cut-in scenarios.

As basic modules of intelligent vehicle, path planning and its tracking have been developed rapidly. However, the trajectory generated by the traditional path-speed decoupled planning method is not feasible in the time domain. In this paper, a method based on improved RRT is proposed to achieve efficient planning and smoothing. In order to realize the matching of track points in space and time domain, closed-loop prediction is adopted to know the actually tracked path more accurately. Taking the nonlinear characteristics of lateral and longitudinal dynamics of the vehicle and the saturation of actuators into account, a unified conditional integral control law is designed to guarantee the global asymptotic stability of the tracking error and to avoid the degradation of actuators performance due to the divergence of the integral operation. Simulations and experiments prove that the proposed planning method is more efficient, the control algorithm can effectively track the planned trajectory, and the prediction method can accurately predict the actual driving trajectory, which is very important for collision detection.

A novel trajectory planning method is proposed in this paper for lane change of autonomous vehicle. Since it is difficult to accurately capture the trajectory of other vehicles, which means the trajectory for autonomous vehicle couldn't always easy to generate quickly. Moreover, the motion planning, as a kind of high-dimensional optimization problem with multiple nonlinear constraints, requires lots of resources to find a right solution. Therefore, we present a trajectory monitoring strategy to keep robust in lane change scenario, which generates the lane change and monitoring trajectory at the same time. If the former does not produce a safe trajectory or is time out, the monitoring trajectory will be taken as the result output. To meet the constraints of vehicle's motion and real-time requirements, B-spline-based method will be employed to plan a continuous curvature path. And RRT-based method works as a supplement for keeping algorithm completeness. Then the monitory trajectory mainly obeys collision-free requirements, which computes deceleration that keeps vehicle stability. The results illustrate that both B-spline-based method and RRT-based could generate curvature continuous and meet the limitation for motion, however, both have the possibility of timeout. Especially, there are challenge to the success rate as environment becomes more complex.