To guarantee vehicle platoon driven pattern in heterogeneous nodes of mixed vehicle platoon (composed of connected and automated vehicles and human-driven vehicles, CAVs and HVs) on curved roads, this study develops a combined longitudinal and lateral controller, which comprises of selecting the key points (KPs) from the trajectory points of detected HVs, correcting the reference trajectory and controlling CAVs with the aid of the corrected KPs. To this end, a new concept, called KPs matrix, is proposed to manage the physical components of every KP by using image processing and vehicle-to-infrastructure (V2I) communication technology. Then, a trajectory correction scheme is presented to suppress the influence of nonstandard human-driven behavior by point set mapping approach in Real Variable Function theory. Furthermore, a novel controller is designed by incorporating the corrected KPs matrix and communication time delay. The stability and convergence of the proposed controller are rigorously analyzed based on the Lyapunov-Krasovskii stability theorem. In addition, extensive experiments are conducted to test the performance including three parts: the first part investigates the feasibility of the corrected KPs matrix by analyzing a video on high-way; the next part illustrates the control performance of the proposed controller on handling the cutting-corner issue (i.e. turning in advance), compared with the conventional controller. Meanwhile, the influence of time delay on the control performance is also analyzed in this study. The last implements driver-in-loop comparative experiments such that the performance of the proposed controller on eliminating the influence of nonstandard human-driven behavior is verified.

Vehicle detection plays an important role in intelligent transportation systems and security. Using the original Single Shot MultiBox Detector (SSD) directly for vehicle detection, lacks accuracy and stability. Moreover, most of the state‐of‐the‐art methods need cost a lot of time to inference. Vehicle detection is often used in complex traffic environments. Therefore, faster detection speed and higher detection accuracy are required. This study is aimed at developing a trade‐off between accuracy and speed vehicle detection framework based on the SSD algorithm. To improve the multi‐scale detection performance of SSD, semantic information, detailed features and receptive fields are combined to propose the feature pyramid enhancement strategy (FPES). On the other hand, the cascade detection mechanism is proposed to strengthen the positioning capability of SSD and an adaptive threshold acquisition method for object detection module (ODM) stage to improve model accuracy. Finally, a more efficient convolutional network is deployed through network slimming. Experimental results demonstrate that the proposed framework achieves state‐of‐the‐art performance on UA‐DETRAC and Udacity benchmarks. Interestingly, the inference time is the lowest for the proposed method than the state‐of‐the‐art methods, promising its application for fast and effective vehicle detection.

Since the unsaturating activation function is unbounded, more complex dynamics may exist in neural networks with this kind of activation function. In this article, monostability and multistability results of almost-periodic solutions are developed for fractional-order neural networks with unsaturating piecewise linear activation functions. Some globally Mittag-Leffler attractive sets are given, and the existence of globally Mittag-Leffler stable almost-periodic solution is demonstrated by using Ascoli-Arzela theorem. In particular, some sufficient conditions are provided to ascertain the multistability of almost-periodic solutions based on locally positively invariant set. It shows that there exists an almost-periodic solution in each positively invariant set, and all trajectories converge to this periodic trajectory in that rectangular area. Two illustrative examples are provided to demonstrate the effectiveness of the proposed sufficient criteria.

In this paper, a novel lane keeping control method for automated vehicles based on human-simulated intelligent control (HSIC) is proposed, which is inspired by human expert drivers’ steering characteristics including good foresight, precise execution and notable intermittency. The novelty of the paper is to introduce the HSIC concept into lateral control of vehicles, which is a multi-mode control scheme implemented by the combination of the feedforward control for curve tracking and act-and-wait control for intermittent error correction. Theoretically, the stabilization problem of the HSIC method is investigated based on the switched system related method. Experiments on the joint simulation platform of PreScan and CarSim show that the newly presented HSIC scheme has better matching performance to the expert driver and good robustness. For automated lane keeping systems, the HSIC method could provide human-like qualities, which may be one of the essential points to determine whether the driver is comfortable or not when the driver hands over the steering authority, improve the transition smoothness in the scenario of human vehicle co-piloting, and eliminate the potential conflicts between manual driving and automated driving vehicles in the future mixed traffic flow.