Mixed platoons, consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), are expected to dominate future roadways. However, achieving substantial improvements in fuel economy and safety under time-varying, mixed traffic conditions in real-world scenarios remains challenging. To this aim, this paper proposes a deep reinforcement learning (DRL) based decentralized control strategy, which consists of two levels: 1) At the methodology level, the hierarchical platoon formation algorithm systematically organizes the mixed platoons into local platoons with uniform car-following patterns and sub-platoons within a generic HDV-CAVs structure, enabling adaptation to time-varying traffic volume and mixed traffic heterogeneity. 2) At the operation level, the HDV-CAVs unit is embedded in the learning environment to capture uncertain HDV behaviors through state and reward propagation channels. Multiple objectives are explicitly integrated into the reward function. A safety-supervised decentralized proximal policy optimization algorithm is developed to enhance safety and training efficiency. Extensive validations demonstrate the effectiveness of the proposed strategy in improving fuel economy and safety under different traffic demand levels and penetration rates.

Highway on-ramps are typical bottlenecks for the deployment of connected and automated vehicles (CAVs). However, few studies have considered time-varying traffic volume and dynamic uncertainties in real-world scenarios. This paper proposes an adaptive robust formation control strategy (ARFC) for CAVs to address these challenges. The ARFC consists of two stages: 1) In the tactical stage, the constraint-oriented platoon formation algorithm classifies approaching CAVs into multiple local virtual platoons (LVP), assigns the collision-free merging sequences and driving modes, thereby enabling the strategy to adapt to time-varying traffic volume. Unified spatial-dependent constraints are formulated to construct this geometry topology, where the spatial dependence ensures collision avoidance in the merging conflict zone. 2) In the operational stage, an adaptive robust control scheme is designed based on the Lyapunov min-max approach, rendering uniform boundedness, uniform ultimate boundedness performance of tracking errors, and string stability of LVP, regardless of time-varying uncertainties. Comprehensive validations demonstrate that the proposed strategy performs effectively under different traffic demands, and improves ride comfort and fuel economy compared to baseline methods.

Weakly supervised object localization (WSOL) aims to localize the entire and well-defined objects only via image-level labels for reducing the need of labor-intensive annotation and mitigating annotation errors. However, many WSOL methods via class activation maps (CAMs) often suffer from incomplete activation and inaccurate boundaries for object localization. In this article, we propose a novel multi-layer decoupling attention localization (MDAL) network to address these issues. We first present a simple yet effective multi-layer comparison decoupling mechanism including a maximum decoupling function and a minimum decoupling function to sufficiently activate and fuse multi-layer features. Then, we introduce the multi-layer maximum decoupling function into the attention modules, and develop a channel attention activation decoupling (CAAD) module and a spatial attention activation decoupling (SAAD) module, which can mine much more useful information for more possible regions' activation. Furthermore, the multi-layer minimum decoupling function is introduced to efficiently fuse and refine multi-layer features, which can suppress the over-activation and background noise. Finally, we develop a joint loss function to train the MDAL network. Experimental results on CUB-200-2011 and ILSVRC2012 demonstrate that our proposed network can provide accurate and complete object localization.

Computation of the orthogonal distance from a given point to an ellipse is the basis of orthogonal distance based ellipse fitting methods. The problem of this orthogonal distance and the corresponding orthogonal contacting point on the ellipse is investigated, and two algorithms, the exact one and the convergent iterative one, are proposed. The exact algorithm utilizes the closed form solution of quartic equations, but is numerically unstable. The iterative algorithm, however, uses Newton's method to solve the equation, and starts from an initial solution that is proven to lead to a convergent iteration. The proposed algorithms are compared in experiments with an existing rival. Although the rival algorithm is slightly faster and more accurate in realistic scenarios, divergence is likely to occur. On the other hand, both our exact and iterative algorithms can reliably produce the solution needed. While the exact algorithm encounters numeric instability, the iterative algorithm is only slightly outperformed by the existing rival in speed and accuracy, but at the same time provides more reliable computation process, thus making it a preferable method for the task.

In order to make use of unlabeled data in hyperspectral images (HSIs), a simple but effective semi-supervised learning method based on convolutional neural network (CNN) is proposed for HSIs classification. First, we define a loss function by integrating a clustering loss function for unlabeled data with softmax loss function for labeled data. Here, the labeled features extracted from CNN are not only used for training classifiers, but also providing anchors to initialize a set of clustering centers via K-means method. Then, all data are used to jointly train deep network for HSIs classification. The experimental results demonstrate that our method can achieve comparative results over the traditional supervised learning method based on CNN. Meanwhile, our method has simple network structure and can be easily trained.

Haze-relevant image features are widely used in haze density perception and transmission estimation for single image dehazing, since hazy images usually have distinct features or characters from clean images. However, little attention has been paid to identify haze-relevant features, and further select some compact but informative image features for image dehazing. In this paper, we propose a novel joint feature selection and transmission estimation model named JFSTE via Deep Belief Network (DBN) for image dehazing. First, we develop a one-to-one linear feature selection layer in the DBN, in which each input feature only connects to one node in next layer with a binary weight. Meanwhile, a simple feature-selection strategy is proposed to determine these binary weights by considering the importance of features and the expected selected feature numbers. Second, in order to identify haze-relevant features of hazy image and narrow down their relevance, the minimum redundancy maximum relevancy is introduced into the joint optimization of our network. A comparative study with state-of-the-art approaches manifests a competitive performance of our proposed method.

Single image defogging algorithms based on prior assumptions or constraints have captured much attention because of their simplicity and practicality. However, they still have some challenges to deal with foggy images captured under weather conditions where these assumptions or constraints may not be effective or efficient enough. In this paper, we aim to develop a novel image defogging algorithm by directly predicting the fog density of recovered images rather than adopting prior assumptions or constraints. In order to achieve this goal, two specific steps are introduced. First, we adopt three fog-relevant statistical features derived from foggy images, and further develop a simple fog density evaluator (SFDE) by creating a linear combination of these fog-relevant features. This proposed evaluator can efficiently perceive the fog density of a single image without reference to a corresponding fog-free image and has a low computational load compared with an existing method. Second, a physics-based mathematical relationship between the transmission and the fog density score of the recovered image is developed via SFDE, thus image defogging can be posed as a minimization problem on the fog density score of the recovered image. As a result, two optimal transmission models, called an optimal transmission model via SFDE (OTSFDE) and a simpler optimal transmission models via SFDE (SOTSFDE), are present to determine the key transmission map for efficient fog removal. Compared to OTSFDE, SOTSFDE has low computational complexity with slight performance degradation. Experimental results demonstrate that the proposed algorithms can effectively remove fog and are not confined by any assumptions or constraints, both quantitatively and qualitatively, compared with some existing algorithms.

Traffic sign recognition is a rather challenging task for intelligent transportation systems since signs in different subsets, e.g., speed limit signs, prohibition signs, and mandatory signs, are very different from each other in color or shape, whereas they share some similarities to the ones in the same subset. Therefore, it is important to integrate different modalities of visual features, such as color and shape, and select discriminative features for better sign description; in addition, it benefits to explore the correlations between the classes of traffic signs to learn the classifiers jointly to improve the generalization performance. In this paper, we propose Multi- Modal tree-structure embedded Multi-Task Learning called M2- tMTL to select discriminative visual features both between and within modalities, as well as the correlated features shared by similar classification tasks. Our method simultaneously introduces two structured sparsity-induced norms into a least squares regression. One of the norms can be used not only to select modality of features but also to conduct within-modality feature selection. Moreover, the hierarchical correlations among the classification tasks are well represented by a tree structure, and therefore, the tree-structure sparsity-induced norm is used for learning the regression coefficients jointly to boost the performance of multi-class traffic sign recognition. Alternating direction method of multipliers (ADMM) is used to efficiently solve the proposed model with guaranteed convergence. Extensive experiments on public benchmark data sets demonstrate that the proposed algorithm leads to a quite interpretable model, and it has better or competitive performance with several state-of-the-art methods but with less computational and memory cost.

State-of-the-art single image dehazing algorithms have some challenges to deal with images captured under complex weather conditions because their assumptions usually do not hold in those situations. In this paper, we develop a deep transmission network for robust single image dehazing. This deep transmission network simultaneously copes with three color channels and local patch information to automatically explore and exploit haze-relevant features in a learning framework. We further explore different network structures and parameter settings to achieve tradeoffs between performance and speed, which shows that color channels information is the most useful haze-relevant feature rather than local information. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets.

Histogram equalisation has been widely used for image enhancement because of its simple implementation and satisfactory performance. However, traditional histogram equalisation uniformly redistributes an entire histogram or multiple piecewise histograms with the same equalisation strategy, which may produce unnatural artefacts, over‐enhancement or under‐enhancement in wide dynamic range dark image enhancement. This study proposes an adaptive extended piecewise histogram equalisation algorithm (AEPHE) for dark image enhancement. First, an original histogram is divided into a group of extended piecewise histograms. Then, an adaptive histogram equalisation, which balances intensity preservation and contrast boosting, is further developed and respectively applied to these extended piecewise histograms. The final histogram for image enhancement is produced by a weighted fusion of these equalised histograms. The experimental results indicate that AEPHE is superior to multiple state‐of‐the‐art algorithms.

We address the problem of vehicle-mounted camera calibration under urban traffic scenes regarding the fact that the traditional calibration methods are practically restricted, since the internal parameters should be calibrated in the laboratory and it is impossible for recalibration that resulted from the parameters drifting or re-focusing when driving on roads. In this paper, we propose to utilize the manual lines lying in Manhattan directions in the scenes to compute their corresponding vanishing points for camera calibration, as the urban traffic scenes are usually man-made and the important lines and signs for driving are typically lying in the Manhattan directions. For “Manhattan world” scenes, where there are plenty of lines lying in Manhattan directions, the lines in the scene are detected automatically, and the clusters corresponding to Manhattan directions are obtained using RANSAC-like methods. For the more general “quasi-Manhattan world” scenes, where only the lines in two directions can be found naturally, while the lines in the other direction are usually detected trivially or even can be hardly detected, we propose a method to estimate the lines in the third direction to improve the vanishing point estimation accuracy. The method proposed is tested on both two types of scenes, and the accuracy and practicability of this method are demonstrated. Furthermore, calibration experiments on both one image and multiple images are conducted, which show that the results can be more accurate when more images are used.