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Traffic congestions are increasingly severe in urban areas, especially at the merging areas of the ramps and the arterial roads. Because of the complex conflict relationship of the vehicles in ramps and arterial roads in terms of time-spatial constraints, it is challenging to coordinate the motion of these vehicles, which may easily cause congestions at the merging areas. The connected and automated vehicles (CAVs) provides potential opportunities to solve this problem. A centralized merging control method for CAVs is proposed in this paper, which can organize the traffic movements in merging areas efficiently and safely. In this method, the merging control model is built to formulate the vehicle coordination problem in merging areas, which is then transformed to the discrete nonlinear optimization form. A simulation model is built to verify the proposed method.

This paper deals with an integrated motor-transmission (IMT) speed tracking control of the connected vehicle when there are controller area network (CAN)-induced delays and denial of service (DOS)-induced delays. A connected vehicle equipped with an IMT system may be attacked through the external network. Therefore, there are two delays on the CAN of the connected vehicle, which are CAN-induced and cyber-attack delays. A DOS attack generates huge delays in CAN and even makes the control system invalid. To address this problem, a robust dynamic output-feedback controller of the IMT speed tracking system considering event-triggered detectors resisting CAN-induced delays and DOS-induced delays is designed. The event-triggered detector is used to reduce the CAN-induced network congestion with appropriate event trigger conditions on the controller input and output channels. CAN-induced delays and DOS-induced delays are modeled by polytopic inclusions using the Taylor series expansion.

In order to reduce the blocking of traffic flow of the signalized intersection on the urban road, for individual vehicles can interact the information with the roadside facilities and the intersection control system under the connected vehicle environment, a speed control strategy in the signalized intersection is proposed. The method consists of two levels, i.e., optimal control range and onboard vehicle speed control. The paper calculates the optimal traffic velocity and vehicles’ arrival time to minimize the total travel time of all vehicles. The purpose of the vehicle passing through the intersection without stops under the guidance of speed guiding strategy could be achieved by analyzing the speed and position of the vehicle and judging whether the vehicle under different driving states can pass. The proposed speed control strategy was analyzed and evaluated in the established simulation environment based on the microscopic traffic simulator VISSIM and Python.

Intelligent Transportation System (ITS) plays an important role in smart city, and accurate short-term traffic flow prediction is a significant part. At present, China’s ITS has developed rapidly, and advanced intelligent transportation systems have been built in major cities, such as Shanghai, Shenzhen and so on. With the promotion of mixed Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) charging systems, the features of the traffic flow data have become richer. Traffic data recorded some information for the vehicles entering and exiting highway toll station including time, location, type, mileage, then we can use historical OD data to do traffic flow prediction, predict the corresponding future exit station traffic flow. Furthermore, due to the deep learning network’s ability to model deep complex non-linear relationship in data, researchers have paid more attention to predict traffic flow using deep learning models in recent years.

The automatic emergency braking (AEB) system and forward collision warning (FCW) system are significant for active safety systems. It can efficiently reduce the rear-end accidents and protect the drivers and pedestrians. The model of an E-class SUV is established with CarSim software, and the control strategy based on fuzzy control is developed with MATLAB/Simulink. Simulation analysis on several typical braking conditions is carded out. The experiment results agree with the analysis results, which indicates that the research method can satisfy the safety requirements of automatic emergency braking system and the accuracy requirement of forward collision warning system.

Urban intersection is the key element to determine the traffic operation of road network. Under the CAVs environment, the roadside control equipment of intersection can communicate with CAVs in real time, collect vehicle state data and optimize traffic control schemes. This paper presents a method for intersection traffic signal control based on deep learning of CAVs data. In addition, intelligent control agent of traffic signal (ICATS) is designed to simulate CAVs. ICATS can perceive real-time changes of traffic flow, model different conditions of intersection and generate the corresponding traffic signal scheme. ICATS used double Q-learning method combination with deep neural network, which is an effective model-independent deep learning algorithm. Moreover, the real traffic data is collected and tested in this paper for evaluating the experiment performance, including vehicle delay, number of passing vehicles, total stop times and passing time.

The recent research on location approaches of the intelligent vehicle based on Light Detection and Ranging (LiDAR) is analyzed in this paper. According to the features of these approaches, it can be divided into three categories: simultaneous localization and mapping (SLAM), offline mapping and online localization (OMOL) and fusion localization (FL). Past research and applications of the main algorithms and critical research scenarios in each localization approaches are reviewed. Three aspects of the current trend in location approaches of the intelligent vehicle based on LiDAR are discussed. Based on object detection, object recognition and object analysis algorithms in the field of deep learning, semantic SLAM and real-time three-dimensional reconstruction are important research trends for SLAM. The performance of robustness and real-time performance of localization algorithm of intelligent vehicles based on LiDAR need to be improved.

Analysis of driver brake behavior parameters of cut-in scenarios is conducted based on naturalistic driving data and accident collision data. The characters of different critical level cut-in cases (normal case, conflict case and collision case) can be obtained by the Time headway (THW) and relative velocity parameter when driver brake initiation. The representative cases, which are selected according to the risk evaluation method, are chosen to evaluate driver’s timing of brake initiation. Using logistical regression method, the cut-in boundary condition model is established with THW and relative velocity parameters. Based on the China Field Operation Test database and China in Depth Accident Study database, the comfortable and safety boundary conditions of cut-in scenarios are established, which is used to optimization the longitudinal control systems of Chinese intelligent connected vehicles.