Platooning is one of the innovations in the automotive industry, which aims to improve the safety and efficiency of automobiles, while alleviating traffic congestion, reducing pollution, and reducing passenger pressure. According to the car-following (CF) theory, a platoon control strategy for autonomous vehicles based on sliding-mode control (SMC) theory is proposed. This strategy can be applied to achieve the rapid platoon forming of multiple autonomous vehicles and maintain the stable state of the vehicle platoon. The Multiple Velocity Difference (MVD) model is selected to describe the positional state of vehicle platoon changing over time. The control target is to converge the error between the actual headway (the distance between front tips of two neighboring cars) and the expected headway to zero while ensuring the stable velocity and acceleration of the platoon. In addition, a hypothetical first car strategy is proposed to improve the control efficiency. Numerical simulation experiments for urban roads and highways are designed, the space-time states of vehicle platoon under different MVD model parameters (non-control strategy) and sliding-mode control strategies are compared. The results show: proposed improved vehicle platoon sliding-mode control strategy can provide a shorter time of forming a platoon and better stability in the simulated environment, and its control effect is better than that of non-control strategy and conventional sliding-mode control strategy. Besides the proposed strategy allows vehicle platoon to quickly reach a stable and controllable state, and it provides an idea for collaborative control of autonomous vehicles.

Understanding the evolution and growth patterns of urban road networks helps to design an efficient and sustainable transport network. The paper proposed a general study framework and analytical workflow based on network theory that could be applied to almost any city to analyze the temporal evolution of road networks. The main tasks follow three steps: vector road network drawing, topology graph generation, and measure classification. Considering data availability and the limitations of existing studies, we took Changchun, China, a middle-sized developing city that is seldom reported in existing studies, as the study area. The research results of Changchun (1912–2017) show the road networks sprawled and densified over time, and the evolution patterns depend on the historical periods and urban planning modes. The evolution of network scales exhibits significant correlation; the population in the city is well correlated with the total road length and car ownership. Each network index also presents specific rules. All road networks are small-world networks, and the arterial roads have been consistent over time; however, the core area changes within the adjacent range but is generally far from the old city. More importantly, we found the correlation between structure and function of the urban road networks in terms of the temporal evolution. However, the temporal evolution pattern shows the correlation varies over time or planning modes, which had not been reported

We propose a motion planning method for automated vehicles (AVs) to complete driving tasks in dynamic traffic scenes. The proposed method aims to generate motion trajectories for an AV after obtaining the surrounding dynamic information and making a preliminary driving decision. The method generates a reference line by interpolating the original waypoints and generates optional trajectories with costs in a prediction interval containing three dimensions (lateral distance, time, and velocity) in the Frenet frame, and filters the optimal trajectory by a series of threshold checks. When calculating the feasibility of optional trajectories, the cost of all optional trajectories after removing obstacle interference shows obvious axisymmetric regularity concerning the reference line. Based on this regularity, we apply the constrained Simulated Annealing Algorithm (SAA) to improve the process of searching for the optimal trajectories. Experiments in three different simulated driving scenarios (speed maintaining, lane changing, and car following) show that the proposed method can efficiently generate safe and comfortable motion trajectories for AVs in dynamic environments. Compared with the method of traversing sampling points in discrete space, the improved motion planning method saves 70.23% of the computation time, and overcomes the limitation of the spatial sampling interval.

To increase the efficiency and precision of large‐scale road network traffic flow prediction, a genetic algorithm‐support vector machine (GA‐SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA‐SVM model and parallel GA‐SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA‐SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

This article presents a Location‐Routing Problem (LRP) model to assist decision makers in emergency logistics. The model attempts to consider the relationship between the location of warehouses and the delivery routes in order to maximize the rescue efficiency. The objective function of the minimization of time and cost is established in the single‐stage LRP model considering different scenarios. The hybrid self‐adaptive bat algorithm (HSABA) is an improved nature‐inspired algorithm for solving this LRP model, hard optimization problem. The HSABA with self‐adaptation mechanism and hybridization mechanism effectively improves the defect of the original BA, that is, trapping into the local optima easily. An example is provided to prove the effectiveness of our model. The studied example shows that the single‐stage LRP model can effectively select supply locations and plan rescue routes faced with different disasters and the HSABA outperforms the basic BA.