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The research on connected vehicles has attracted much interest of governments and research institutions in these days. As researchers who have been deeply intrigued, we are particularly interested in investigating the lane changing issues of connected buses when exiting bus stops. In order to examine the utilities of both the buses and social vehicles in a connected environment, we employ the game theory to analyze the interactions between them. A two-player, non-cooperative, non-zero-sum game model was built in this paper. We analyzed 5 different scenarios for bus exiting at bus bay stops and curbside bus stops. The payoffs considered in the game model were mainly from the perspective of safety and time. Besides the commonly studied safety issues caused by collisions, we supplement the existing literature by adding on non-collision injuries caused by improper driving behaviors (e.g. excessive acceleration).

Low-speed electric vehicles (LSEV) are very popular in the less-developed area in China, and the industrial scale of LSEV is gradually expanding due to their great fit to the short-distance urban and rural travel and low purchase cost. However, the R&D strength and design investment of LSEV enterprises are uneven, and there are generally problems of unreasonable body structure and poor collision safety performance among LSEVs. In the transition period when the national mandatory standards are being developed, in order to make the LSEV enterprises complete the technology upgrade, this paper will build a FEM model based on a certain type of mass production vehicle and conduct collision simulation test to analyze the weak structure of the original structure and proposed a structural optimization.

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.

AEB two-wheeler system is an active safety technology which developed for avoiding two-wheeler accidents. With a large number of two wheelers, China needs to conduct research and analysis of this system. In this paper, real traffic accident data in China are used to analyze the possible influence of AEB two-wheeler system on traffic accidents. The paper chooses real passenger car impact two wheeler accidents happen in China, and gets the characteristics of data analysis. Then the paper analysis the influence of AEB system using position data when TTC=3s. According to the analysis, the detection range of 90 meters and the detection angle of 45 degrees can improve the recognition rate of day crossing accident by 17.7%, night crossing accident by 21.5% and longitudinal accident by 38.8%. The paper provides effective support for the development and test of AEB two-wheeler system in China. Car-to-two-wheeler accident AEB two-wheeler system Influence of the accident

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.

The databases of traffic accidents contain many association rules related to the factors of dangerous driving scenes (DDS). In order to mine multi-dimensional weighted association rules in the databases according to the impact of each factor on traffic accidents, a hybrid data mining algorithm is proposed. It includes the Analytic Hierarchy Process (AHP) and Multi-Dimensional Weighted FP-Growth algorithm (MWFP-Growth). The AHP performs a hierarchical analysis of the DDS and establishes the judgment matrix between layers according to the method of pairwise comparison. After the eigenvectors related to the maximum eigenvalues of the judgment matrix are calculated, the weight of each factor is determined, and a multi-dimensional weighted dataset is established; The MWFP-Growth algorithm is used to mine association rules in the multi-dimensional weighted dataset.

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.

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.