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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.

In order to solve the coupling problems between braking safety, economical efficiency of braking and the comfort of drivers, a braking control strategy based on Electronically Controlled Braking System (EBS) and intelligent network technology under non-emergency braking conditions is proposed. The controller utilizes the intelligent network technology’s characteristics of the workshop communication to obtain the driving environment information of the current vehicle firstly, and then calculate the optimal braking deceleration of the vehicle based on optimal control method. The strategy will distribute the braking force according to the ideal braking force distribution condition based on the EBS according to the braking deceleration; the braking force will be converted to braking pressure according to brake characteristics. Computer co-simulations of the proposed strategy are performed, the strategy is verified under different initial speeds.

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.

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.

In this paper, a torque vectoring controller is proposed for the distributed-drive electric vehicles to simultaneously prevent loss of traction from excessive wheel slip and enhance vehicle lateral stability. In order to know the expected generation of direct yaw moment for vehicle lateral stability, this study provides two computationally efficient laws for comparisons which are fuzzy logic method and sliding mode method. Besides, a control-oriented model is formulated to describe the plant in mathematics. To satisfy the requirement of direct yaw-moment output and the control input constraints, sequential quadratic programming is adopted to find the optimal distribution for the drive wheel torques aiming at achieving the maximum tire grip margin. Moreover, a proportional-integral controller is designed to compensate the drive wheel torque for the purpose of preventing the excessive wheel slip.