Recently, with the increasing difficulty of parking in cities, the wireless vehicle detectors (WVDs) based on Internet of Thing (IoT) technology and magnetic sensor have been widely used to obtain parking space status data, which lay the foundation for smart parking management system (SPMS). However, there is a weak magnetic region under every vehicle where the magnetic variation is even smaller than that distorted by adjacent vehicles, which leads to the accuracy decrease of the magnetometer-based vehicle detection algorithm. To improve the performance of the WVDs, an IoT-Driven vehicle detection method which combines the heterogeneous data feature of UWB channel with that of magnetic signal is proposed in this paper. In the proposed method, length of the propagation path and signature of the channel impulse response (CIR) with respect to the vehicles, which can be obtained from UWB modules, are introduced to achieve vehicle detection. Moreover, considering that the energy consumption of UWB modules is much higher than that of magnetic sensors, the UWB-based vehicle detection algorithm is only activated when the signature of magnetic signals cannot enable magnetometer-based vehicle detection algorithm to output an accurate decision. Finally, the proposed method was evaluated in a commercial parking lot under different conditions. The experiments show that it has a detection accuracy of 98.81% when the sampling rate of magnetic sensor is 1 Hz.

Research on trajectory tracking is crucial for the development of autonomous vehicles. This paper presents a trajectory tracking scheme by utilizing model predictive control (MPC) and preview-follower theory (PFT), which includes a reference generation module and a MPC controller. The reference generation module could calculate reference lateral acceleration at the preview point by PFT to update state variables, and generate a reference yaw rate in each prediction point. Since the preview range is increased, PFT makes the calculation of yaw rate more accurate. Through physical constraints, the MPC controller can achieve the best tracking of the reference path. The MPC problem is formulated as a linear time-varying (LTV) MPC controller to achieve a predictive model from nonlinear vehicle dynamics to continuous online linearization. The MPC-PFT controller method performs well by increasing the effective length of the reference path. Compared with MPC and PFT controllers, the effectiveness and robustness of the proposed method are proved by simulations of two typical working conditions.

In this paper, the original Choquet integral is generalized as a fuzzified Choquet integral with a fuzzy-valued integrand (FCIFI), which supports a fuzzy-valued integrand and an integration result. The calculation of the FCIFI is established on the Choquet integral with an interval-valued integrand (CIII). The definitions, properties, and calculation algorithms of the CIII and the FCIFI are discussed and proposed in this paper. As a specific application scheme, we designed a CIII regression model for the regression problems involving interval-valued data. This CIII regression model has a self-learning ability through a double genetic algorithm. Finally, a daily temperature predictor based on the CIII regression model is discussed, where a series of experiments is implemented to validate the performance of the predictor by real weather records from the Hong Kong observatory.

Establishing a symmetrical model of surrounding vehicles and accurately obtaining the driving state of the surrounding vehicles in the driving environment can improve the safety of driving, which is an important issue that needs to be considered in the automatic driving system or auxiliary driving system. Therefore, we propose an adaptive unscented Kalman filter algorithm based on Interacting Multiple Model (IMM) theory to estimate the state of target vehicle in the high-speed driving environment. To be specific, we use the Constant Turn Rate and Acceleration (CTRA) theory to establish the target vehicle kinematics model, simultaneously, in order to overcome the problem of estimator failure when the yaw rate is close to zero, a simplified version of the CTRA model is also introduced into the estimation process. In addition, the parameter adaptation strategy is added, so the proposed estimator can overcome the uncertainty of the noise model and improve its accuracy. Finally, the effectiveness of proposed state estimation algorithm is verified on the Carsim and Simulink co-simulation platform. The results of simulations and experiments show that the accuracy and stability of IMM-based algorithm is better than the single-model algorithm in different scenarios, and the parameter adaptation strategy brings performance improvement.

Ultrafast fiber lasers with a wide spectral range have huge demands for many applications. In this paper, magnetron sputtering deposition (MSD) technique was utilized to prepare a new broadband microfiber based Sb thin film saturable absorbent (SA), and ultrafast pulses in both 1.5 and 2 μm wavebands were generated with the integrated microfiber-based Sb thin film SA in erbium-doped fiber laser (EDFL) and thulium-doped fiber laser (TDFL), respectively. Pulse duration of 544 fs / 972 fs and output power of 21.76 mW / 36.1 mW centered at 1557 nm / 1892 nm were respectively produced from stable mode-locked EDFL and TDFL. To the best our knowledge, this is the first time that ultrafast fiber lasers including both 1.5 μm and 2 μm wavebands were mode locked by a single microfiber-based Sb thin film SA.

The Choquet integral is a powerful aggregation tool in information fusing and data mining. In this paper, a generalized nonlinear classification model based on single Choquet integral is summarized, and a novel generalized nonlinear classification model based on cross-oriented Choquet integrals is presented. Compared to the classification model based on single Choquet integral, a couple of Choquet integral are used to achieve the classification boundaries which can classify data in such situation as one class surrounding another one in a high dimensional space. The classification problems come down to properly specifying the fuzzy measure on which the Choquet integral(s) are defined and the classifying boundaries by which the different classes are separated. The values of these unknown parameters are optimally determined by evolutionary computation. The performance of these models are compared and validated with existed methods on a number of benchmark datasets.