In this study, a Human-Lead-Platoon CACC ((HLP-CACC) controller is proposed for connected and automated vehicles to “include” human drivers in platooning process. The goal is to form a platoon between automated vehicles and human drivers so that turbulence caused by human drivers could be smoothed out by automated vehicles. Unlike the conventional CACC where only longitudinal control is automated, the proposed HLP-CACC regulates both longitudinally and laterally. In other words, the followers in an HLP-CACC platoon are fully autonomous. The controller is formulated utilizing model predictive control (MPC) solved by Chang-Hu’s method. The technology has the following advantages: 1) take advantage of human drivers’ perception to enable conditional full autonomy; 2) accommodate actuator delay in system dynamics to improve actuator control accuracy; 3) automates both longitudinally and laterally; and 4) ensures string stability in partially connected and automated vehicles environment. Both simulation tests and field tests were conducted to verify the effectiveness of the proposed algorithm. Four scenarios, including straight cruising, lane changing, U-turn and circling were tested. Sensitivity analysis was conducted for speed, turning radius, communication delay and oscillation acceleration. The results confirm that the proposed CACC controller is ready for field implementation. The computation time of the proposed optimal control is approximately $4~\sim ~8$ milliseconds when running on an NVIDIA Drive PX 2 computer. Under the control of the proposed HLP-CACC, maximum longitudinal error and lateral error are both within 40 cm.

Intelligent transportation systems (ITSs) play an increasingly important role in traffic management and traffic safety. Smart cameras are the most widely used sensors in ITSs. However, cameras suffer from a reduction in detection and positioning accuracy due to target occlusion and external environmental interference, which has become a bottleneck restricting ITS development. This work designs a stable perception system based on a millimeter-wave radar and camera to address these problems. Radar has better ranging accuracy and weather robustness, which is a better complement to camera perception. Based on an improved Gaussian mixture probability hypothesis density (GM-PHD) filter, we also propose an optimal attribute fusion algorithm for target detection and tracking. The algorithm selects the sensors’ optimal measurement attributes to improve the localization accuracy while introducing an adaptive attenuation function and loss tags to ensure the continuity of the target trajectory. The verification experiments of the algorithm and the perception system demonstrate that our scheme can steadily output the classification and high-precision localization information of the target. The proposed framework could guide the design of safer and more efficient ITSs with low costs.

Background: The precise characterization of individual tumors and immune microenvironments using transcriptome sequencing has provided a great opportunity for successful personalized cancer treatment. However, the cancer treatment response is often characterized by in vitro assays or bulk transcriptomes that neglect the heterogeneity of malignant tumors in vivo and the immune microenvironment, motivating the need to use single-cell transcriptomes for personalized cancer treatment. Methods: Here, we present comboSC, a computational proof-of-concept study to explore the feasibility of personalized cancer combination therapy optimization using single-cell transcriptomes. ComboSC provides a workable solution to stratify individual patient samples based on quantitative evaluation of their personalized immune microenvironment with single-cell RNA sequencing and maximize the translational potential of in vitro cellular response to unify the identification of synergistic drug/small molecule combinations or small molecules that can be paired with immune checkpoint inhibitors to boost immunotherapy from a large collection of small molecules and drugs, and finally prioritize them for personalized clinical use based on bipartition graph optimization. Results: We apply comboSC to publicly available 119 single-cell transcriptome data from a comprehensive set of 119 tumor samples from 15 cancer types and validate the predicted drug combination with literature evidence, mining clinical trial data, perturbation of patient-derived cell line data, and finally in-vivo samples. Conclusions: Overall, comboSC provides a feasible and one-stop computational prototype and a proof-of-concept study to predict potential drug combinations for further experimental validation and clinical usage using the single-cell transcriptome, which will facilitate and accelerate personalized tumor treatment by reducing screening time from a large drug combination space and saving valuable treatment time for individual patients. A user-friendly web server of comboSC for both clinical and research users is available at www.combosc.top. The source code is also available on GitHub at https://github.com/bm2-lab/comboSC.

A three-dimensional (3-D) positioning system on the basis of bat algorithm (BA) using visible light communication is proposed in this work. BA is a global optimization algorithm that can be used to solve the indoor positioning problem. In BA, the positioning process is considered to be a process of searching for the receiver by many bats in an indoor space of $3\mathrm{m}\times 3\mathrm{m}\times 4\mathrm{m}$. Therefore, taking advantage of the bat search algorithm, 3-D low-complexity and high-precision indoor positioning is able to be actualized when the number of iterations reaches a certain condition. The simulation results show that the system can achieve high-precision positioning at different signal-to-noise ratios. Meanwhile, a trajectory tracking experiment is carried out to validate the good performance of this positioning system. The proposed algorithm is a positioning method with good potential in various positioning applications.

Caching is a promising approach to address the backhaul traffic congestion problem and boost throughput in fog radio access networks. In this paper, we investigate a proactive probabilistic caching optimization in wireless fog radio access network where multiple users request different files from multiple base station (BSs). To assess the performance, we first derive the analytical results of successful transmission probability (STP) using tools of stochastic geometry. With the derived closed-form STP expressions, our objective is to optimize the proactive probabilistic caching distribution to maximize the STP. To reduce the computational complexity, we specially discuss the optimization in high signal-to-noise ratio. We propose a projection gradient method to get a local optimal solution. Simulation results show that the caching placement optimized by our proposed algorithm increases the STP by about 18% over the best existing caching distribution when $\rm{SNR}=10\mathbf{dB}$ with user density $\lambda_u=0.09$ , base station density $\lambda_b=0.1$ and file number $N=100$ .

Heterogeneous wireless networks (HWNs), composed of densely deployed base stations of different types with various radio access technologies, have become a prevailing trend to accommodate ever increasing traffic demand. Nowadays, users rely heavily on HWNs for ubiquitous network access to support critical applications such as financial transactions, e-health, and public safety. Cyber risks, representing one of the most significant threats to network security and reliability, are increasing in severity. To address this problem, this article introduces the concept of cyber insurance to transfer the cyber risks in HWNs to a third-party insurer. First, a review of the enabling technologies for HWNs and their vulnerabilities to cyber risks is presented. Then the fundamentals of cyber insurance are introduced, and subsequently, a cyber insurance framework for HWNs is presented. Finally, open issues are discussed and the challenges are highlighted for integrating cyber insurance as a service of next generation HWNs.