Due to the ubiquitous deployment of WiFi infrastructure, numerous studies have employed WiFi RSSI fingerprinting for indoor localization. However, fingerprinting methods necessitate labour-intensive site surveys for fingerprint collection and location annotation. To address these limitations, we propose WiMU, a real-time indoor localization system that integrates WiFi and inertial measurement unit (IMU) data to enhance the real-time performance and accuracy of localization. WiMU operates on commodity WiFi infrastructure without the need for additional hardware, leveraging crowd-sourced user trajectories to learn spatial representations of access points (APs). These representations can be fine-tuned with minimal labeled data to support effective localization. Extensive evaluations demonstrate that WiMU reduces the cost of building an indoor localization system while ensuring high positioning accuracy, paving the way for the large-scale deployment of real-time indoor localization systems.

Motion planning directly in the spatiotemporal dimension can generate trajectories of higher quality compared to decoupled methods for autonomous driving. However, it requires a greater amount of computational resources. This paper proposes an efficient motion planning method based on convolution in the spatiotemporal dimension, which takes into account the uncertainty of localization and obstacle intention. Firstly, a three-dimensional probability occupancy grid map with uncertainty is constructed based on prediction results. Secondly, convolution kernels are generated considering the contour, heading angle and localization uncertainty of the ego vehicle. Thirdly, single-channel multi-output convolutions are performed between the probability occupancy grid map and the kernels to generate the four-dimensional feature map. Finally, a collision avoidance algorithm based on the feature map is proposed to obtain the optimal trajectory, which uses the hybrid A* algorithm. The chance constraint and the vehicle kinematics are taken into account in the motion planning. In simulation experiments, the safety performance, computational efficiency and rationality of the motion planning are compared and analyzed, and the proposed method performs superiorly. In addition, real-world experiments verify the feasibility of the proposed method.

Semi-supervised semantic segmentation aims to maximize the training performance for a limited annotation cost. Existing methods such as cross pseudo supervision have shown excellent performance, yet ignore potential information interactions between labeled and unlabeled data, and suffer from misleading incorrect pseudo labels. This paper takes two ways to improve each of these shortcomings. Firstly, we perform feature-level mixing and cross-decoupling using labeled and unlabeled data to establish potential interactions between the two types of data. Secondly, an uncertainty-aware loss re-weighting method based on information entropy is used to mitigate the negative effects of incorrect pseudo labels. Experimentally, our method further improves the previous cross pseudo supervision method with competitive performance on PASCAL VOC 2012 dataset under various data partition protocols.

This paper proposes a convolution-based method for reconfiguring highly constrained environments, which considers the contour and heading of an autonomous vehicle. The vehicle with possible different heading angles is taken as the kernels. The multiple convolutions between the kernels and the environment are performed to generate a three-dimensional grid map, which significantly improves the computational efficiency of the collision detection algorithm. Moreover, a hierarchical and multistage trajectory planning method based on the reconfigured grid map is proposed. The superiority of the proposed method is verified by comparative simulations and real-time experiments.

The updating of high-definition (HD) map is notably necessary to autonomous vehicle, ensuring the normal operation of the vehicle. This paper proposes an incremental fusing method for HD map updating, which can continuously improve the accuracy of the map during the running of vehicle. To describe the accuracy of map, the concept of position confidence and semantic confidence are presented. A framework of HD map updating is described, enhancing the frequency of updating and saving the cost. We analyze the map fusing model and propose a fusing algorithm based on Kalman filtering, which can increase the position and semantic confidence of map. Meanwhile, time damping term is contained in the fusing algorithm, which can make the map element approach the true value much faster when the real environment has been changed slightly. The experimental results show that the method proposed is effective.

This paper presents a two-time-scale control method to optimize the energy consumption of high-performance-computing data centers through dynamic frequency scaling of processors, tasks assignment, and cooling supplement. First, the steady and dynamical models of the data center are built, which reflect the computational interactions and thermal relationship among the components of the data center. Next, the energy minimization problem for processing a parallel task is divided into two parts that correspond to the steady thermal model and the dynamic thermal one. Then, the problem is solved in a two-time-scale manner, i.e., the optimization of task assignment and processing frequency is considered in steady thermal environment, and the optimization of cooling supplement is achieved in dynamic thermal environment. Finally, simulations of a real task trace are carried out, which demonstrate that the proposed method can significantly improve energy efficiency while guaranteeing the thermal constraints of the data center.