Shan Zhang

Shan Zhang, Jiayin Chen, Feng Lyu et al.

Embedded with advanced sensors, cameras and processors, the emerging automated driving vehicles are capable of sensing the environment and conducting automobile operation, paving the way to modern intelligent transportation systems (ITS) with high safety and efficiency. On the other hand, vehicular communication networks (VCNs) connect vehicles, infrastructures, clouds, and all other devices with communication modules, whereby vehicles can obtain local and global information to make intelligent operation decisions. Although the sensing-based automated driving technologies and VCNs have been investigated independently, their interactions and mutual benefits are still underdeveloped. In this article, we argue that VCNs have attractive potentials to enhance the on-board sensing-based automated vehicles from different perspectives, such as driving safety, transportation efficiency, as well as customer experiences. A case study is conducted to demonstrate that the traffic jam can be relieved at intersections with automated driving vehicles coordinated with each other through VCNs. Furthermore, we highlight the critical yet interesting issues for future research, based on the specific requirements posed by automated driving on VCNs.

Steven Shaw, Kanishka Tyagi, Shan Zhang

Many radar signal processing methodologies are being developed for critical road safety perception tasks. Unfortunately, these signal processing algorithms are often poorly suited to run on embedded hardware accelerators used in automobiles. Conversely, end-to-end machine learning (ML) approaches better exploit the performance gains brought by specialized accelerators. In this paper, we propose a teacher-student knowledge distillation approach for low-level radar perception tasks. We utilize a hybrid model for stationary object detection as a teacher to train an end-to-end ML student model. The student can efficiently harness embedded compute for real-time deployment. We demonstrate that the proposed student model runs at speeds 100x faster than the teacher model.

Qiang Chen, Xiangbo Su, Xinyu Zhang et al.

In this paper, we present a light-weight detection transformer, LW-DETR, which outperforms YOLOs for real-time object detection. The architecture is a simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our approach leverages recent advanced techniques, such as training-effective techniques, e.g., improved loss and pretraining, and interleaved window and global attentions for reducing the ViT encoder complexity. We improve the ViT encoder by aggregating multi-level feature maps, and the intermediate and final feature maps in the ViT encoder, forming richer feature maps, and introduce window-major feature map organization for improving the efficiency of interleaved attention computation. Experimental results demonstrate that the proposed approach is superior over existing real-time detectors, e.g., YOLO and its variants, on COCO and other benchmark datasets. Code and models are available at (https://github.com/Atten4Vis/LW-DETR).

Junjie Wang, Feng Gao, Junyu Dong et al.

Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality samples for parameter optimization. The noise in pseudo-labels inevitably affects the final change detection performance. To solve the problem, we propose a Graph-based Knowledge Supplement Network (GKSNet). To be more specific, we extract discriminative information from the existing labeled dataset as additional knowledge, to suppress the adverse effects of noisy samples to some extent. Afterwards, we design a graph transfer module to distill contextual information attentively from the labeled dataset to the target dataset, which bridges feature correlation between datasets. To validate the proposed method, we conducted extensive experiments on four SAR datasets, which demonstrated the superiority of the proposed GKSNet as compared to several state-of-the-art baselines. Our codes are available at https://github.com/summitgao/SAR_CD_GKSNet.

Nuwan Janaka, Xinke Wu, Shan Zhang et al.

It is common for people to engage in information acquisition tasks while on the move. To understand how users' visual behaviors influence microlearning, a form of mobile information acquisition, we conducted a shadowing study with 8 participants and identified three common visual behaviors: 'glance', 'inspect', and 'drift'. We found that 'drift' best supports mobile information acquisition. We also identified four user-related factors that can influence the utilization of mobile information acquisition opportunities: situational awareness, switching costs, ongoing cognitive processes, and awareness of opportunities. We further examined how these user-related factors interplay with device-related factors through a technology probe with 20 participants using mobile phones and optical head-mounted displays (OHMDs). Results indicate that different device platforms significantly influence how mobile information acquisition opportunities are used: OHMDs can better support mobile information acquisition when visual attention is fragmented. OHMDs facilitate shorter visual switch-times between the task and surroundings, which reduces the mental barrier of task transition. Mobile phones, on the other hand, provide a more focused experience in more stable surroundings. Based on these findings, we discuss trade-offs and design implications for supporting information acquisition tasks on the move.