Virtual testing plays a vital role in developing connected and automated vehicles. To ensure the safety of CAV, millions of miles of testing are required. The scenario library defines the parameter of each testing scenario, which reduces testing by merging similar scenarios and focusing on challenging scenarios. However, current scenario generation methods still bear some disadvantages, such as lack of data and low efficiency. In order to overcome the lack of data, this paper proposed a genetic algorithm-based framework for the cut-in event and car-following event. In this study, the genetic algorithm searches for one of the most challenging scenarios within the boundaries. The challenging scenario is acquired by the brute force search algorithm which searches from the start scenario. The result shows the ratio of accident scenarios to challenging scenarios in our scenario set is more than 40%. Compared with natural driving data scenarios, our method performs well without natural driving data. In addition, the genetic algorithm converges in ten iterations which indicates its efficiency and suitability in scenario generation. More specifically, whole challenging scenarios which probably occur in the real world are collected in the scenario set.

Enhancing the quality of the images acquired under the water environments is crucial in many broadcast technologies. As the richness of the features generated by deep underwater image enhancement networks improves, the visual signals with higher qualities can be yielded. In view of this, in this paper, we propose a new deep network for the task of underwater image enhancement, in which the network feature generation process is guided by the prior information obtained from various underwater medium transmission map and atmospheric light estimation methods. Further, in order to obtain high values for different image quality assessment metrics associated with the images produced by the proposed network, we introduce a multi-stage training process for our network. In the first stage, the proposed network is trained with the conventional supervised learning technique, whereas, in the second stage, the training process of the network is carried out by the adversarial learning technique. Finally, in the third stage, the training of the network obtained by the conventional supervised learning is continued by the guidance of the one trained by the adversarial learning technique. In the development of the adversarial learning-based stage of our network, we propose a novel multi-discriminator generative adversarial network, which is able to produce images with more realistic textures and structures. The proposed multi-discriminator generative adversarial network employs the discrimination process between the real and fake data in various underwater environment color spaces. The results of different experimentations show the effectiveness of the proposed scheme in restoring the high-quality images compared to the other state-of-the-art deep underwater image enhancement networks.

Many aerial images with similar appearances have different but correlated scene labels, which causes the label ambiguity. Label distribution learning (LDL) can express label ambiguity by giving each sample a label distribution. Thus, a sample contributes to the learning of its ground-truth label as well as correlated labels, which improve data utilization. LDL has gained success in many fields, such as age estimation, in which label ambiguity can be easily modeled on the basis of the prior knowledge about local sample similarity and global label correlations. However, LDL has never been applied to scene classification, because there is no knowledge about the local similarity and label correlations and thus it is hard to model label ambiguity. In this paper, we uncover the sample neighbors that cause label ambiguity by jointly capturing the local similarity and label correlations and propose neighbor-based LDL (N-LDL) for aerial scene classification. We define a subspace learning problem, which formulates the neighboring relations as a coefficient matrix that is regularized by a sparse constraint and label correlations. The sparse constraint provides a few nearest neighbors, which captures local similarity. The label correlations are predefined according to the confusion matrices on validation sets. During subspace learning, the neighboring relations are encouraged to agree with the label correlations, which ensures that the uncovered neighbors have correlated labels. Finally, the label propagation among the neighbors forms the label distributions, which leads to label smoothing in terms of label ambiguity. The label distributions are used to train convolutional neural networks (CNNs). Experiments on the aerial image dataset (AID) and NWPU_RESISC45 (NR) datasets demonstrate that using the label distributions clearly improves the classification performance by assisting feature learning and mitigating over-fitting problems, and our method achieves state-of-the-art performance.

Diffusion models have provided the state-of-the-art performances for different computer vision tasks, including the task of underwater image enhancement. One of the challenges in the task of underwater image enhancement is that various spatial regions of the image require different restoration techniques. In order to address this, we propose a novel diffusion-based underwater image enhancement network, in which by employing the two ideas of uncertainty-aware learning and feature recalibration based on the color tones dominated in the underwater environments, it is able to provide superior performances. Specifically, the former idea strives to process various spatial regions of the underwater image based on their restoration uncertainty, while the latter technique recalibrates the features generated by the diffusion model by taking the various color tones in the underwater environments into consideration. The results of different experimentations show the superiority of the proposed diffusion-based model over the other state-of-the-art underwater image enhancement networks.