Search

Recently, low-rank tensor completion (LRTC) methods by exploiting the global low-rankness of the target tensor have shown great potential for traffic data imputation. However, in real-world transportation networks, traffic data usually suffer from more complicated structural missing patterns than random-missing patterns, e.g., tube-missing patterns due to disruptions in wireless connections or slice-missing mechanism caused by sensor maintenance. As the naturally low-rank structure of traffic data in several missing scenarios, the existing LRTC methods indeed refrain from desirable performance for imputing traffic data. To tackle the complicated missing scenarios, we propose a convolutional low-rank tensor representation (CLRTR). Especially, CLRTR represents each unfolding matrix of the tensor as a sum of convolutions between two-dimensional (2D) filters and the corresponding low-rank coefficients, which allows us to simultaneously reveal the local patterns and the low-rankness of traffic data. Based on the CLRTR, we introduce the corresponding low-rank metric CLRTR-rank. Based on the suggested low-rank metric, we propose a traffic data imputation model that is well-suited to the complicated missing data scenarios. To implement the resultant imputation model, we design the alternating direction method of multipliers (ADMM) based algorithm with a theoretical convergence guarantee. Extensive numerical experiments on several real-world traffic datasets for both traffic data imputation and downstream traffic data prediction highlight the superiority of our model over the existing state-of-the-art matrix/tensor models for extensive missing scenarios.

Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivor rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct benchmark experiments on the uni-modality of radar and camera, as well as the fused modalities. Experimental results demonstrate that 4D radar-camera fusion can considerably improve the accuracy and robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io.