The active crash avoidance system of intelligent vehicles faces challenges in accurately triggering and achieving effective multiobjective coordination control in complex driving environments characterized by multiple vehicles and variable road conditions, thereby posing a threat to driving safety for human. To address those issues, this article introduces a decision-making, path planning, and tracking integrated predictive control method (DMIC). First, DMIC incorporates vehicle actuation system characteristics, road conditions, and environmental information to design a dynamic characteristic-based risk indicator, which is applied to design different control modes. DMIC then applies continuous activation functions to activate optimized states and control inputs under various control modes, forming an integrated event-triggered continuous decision-making objective function. Afterward, DMIC employs the receding horizon optimization to calculate the front-wheel steering angle and wheel torques based on the integrated predictive model and time-varying constraints. Verification on a driver-in-the-loop (DiL) platform demonstrates that DMIC can accurately and smoothly switch among different optimization states, ensuring crash avoidance with arbitrary approaching vehicles under varying road conditions, thereby maintaining smooth vehicle responses, enhancing the driving stability and ride comfort, while meeting real-time and robustness requirements.

Adaptive cruise control (ACC) systems currently face the challenge of balancing tracking performance and avoiding collisions with arbitrary cut-in vehicles from different lanes. The multiobjective ACC proposed in this article is based on a novel integrated structure. The novel integrated ACC structure consists of an adaptive controller and the associated switching mechanism. The controller combines the upper and lower layers, which are common in today's hierarchical controllers. The switching mechanism is designed to switch between different modes to avoid collisions and maintain tracking capability in complex driving scenarios. Complex scenarios are designed to validate the integrated structure's effectiveness, real-time performance, and robustness, and a driver-in-the-loop platform is established. The results indicate that the novel integrated structure is capable of tracking the preceding vehicle accurately while avoiding colliding with the surrounding vehicle from various directions, thereby ensuring vehicle stability under varying road adhesion and system uncertainties.

MOTIVATION: The interpretation of mass spectrometry (MS) data is a crucial step in proteomics analysis, and the identification of post-translational modifications (PTMs) is vital for the understanding of the regulation mechanism of the living system. Among various PTMs, glycosylation is one of the most diverse ones. Though many search engines have been developed to decipher proteomic data, some of them are difficult to operate and have poor performance on glycoproteomic datasets compared to advanced glycoproteomic software. RESULTS: To simplify the analysis of proteomic datasets, especially O-glycoproteomic datasets, here, we present a user-friendly proteomic database search platform, MS-Decipher, for the identification of peptides from MS data. Two scoring schemes can be chosen for peptide-spectra matching. It was found that MS-Decipher had the same sensitivity and confidence in peptide identification compared to traditional database searching software. In addition, a special search mode, O-Search, is integrated into MS-Decipher to identify O-glycopeptides for O-glycoproteomic analysis. Compared with Mascot, MetaMorpheus and MSFragger, MS-Decipher can obtain about 139.9%, 48.8% and 6.9% more O-glycopeptide-spectrum matches. A useful tool is provided in MS-Decipher for the visualization of O-glycopeptide-spectra matches. MS-Decipher has a user-friendly graphical user interface, making it easier to operate. Several file formats are available in the searching and validation steps. MS-Decipher is implemented with Java, and can be used cross-platform. AVAILABILITY AND IMPLEMENTATION: MS-Decipher is freely available at https://github.com/DICP-1809/MS-Decipher for academic use. For detailed implementation steps, please see the user guide. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In some practical scenarios such as tour guiding and children babysitting, one mobile device held by tour guides or parents need to know the distance and direction of another nearby mobile device held by tourist or children. However, to date, most existing pedestrian localization methods rely on a fixed external infrastructure, such as a global positioning system(GPS) or pre-deployed wifi access points to provide Localization service for mobile devices. Such methods are constrained either by limited GPS coverage or by complicated set-up procedures. We observe that, when two devices are moving, the change of their positions leads to the change of distance between them. Given the same movements, different relative locations between devices lead to different distance changes. Besides, the distance between and relative movement of devices can be measured by two phone-embedded sensors respectively. This motivates us to exploit the relative localization method by merely two mobile devices. In this work, we present Phone-Radar, which is an infrastructure-free device-to-device localization system. According to the propagation model of wireless signals, the change of distance between devices are modeled by the change of wireless signal strength between them. The movements of devices are recorded by the inertial sensors using step-counting method. We further study the relationship among the initial relative locations between the two devices, their relative movements and the change of received signal strength measurements. Moreover, we implement the proposed method and measure its performance under real world conditions. The testbed experiments show the efficiency of our proposed method.