Dedicated short-range communication (DSRC) and 4G-LTE are two widely used candidate schemes for Connected Vehicle (CV) applications. It is thus of great necessity to compare these two most viable communication standards and clarify which one can meet the requirements of most V2X scenarios with respect to road safety, traffic efficiency, and infotainment. To the best of our knowledge, almost all the existing studies on comparing the feasibility of DRSC or LTE in V2X applications use software-based simulations, which may not represent realistic constraints. In this paper, a Connected Vehicle test-bed is established, which integrates the DSRC roadside units, 4G-LTE cellular communication stations, and vehicular on-board terminals. Three Connected Vehicle application scenarios are set as Collision Avoidance, Traffic Text Message Broadcast, and Multimedia File Download, respectively. A software tool is developed to record GPS positions/velocities of the test vehicles and record certain wireless communication performance indicators. The experiments have been carried out under different conditions. According to our results, 4G-LTE is more preferred for the nonsafety applications, such as traffic information transmission, file download, or Internet accessing, which does not necessarily require the high-speed real-time communication, while for the safety applications, such as Collision Avoidance or electronic traffic sign, DSRC outperforms the 4G-LTE.

Vehicle merging is one of the main causes of reduced traffic efficiency, increased risk of collision, and fuel consumption. Connected and automated vehicles (CAVs) can improve traffic efficiency, increase safety, and reduce the negative environmental impacts through effective communication and control. Therefore, to improve the traffic efficiency and reduce the fuel consumption in on-ramp scenarios, this paper addresses the global and optimal coordination of the CAVs in a merging zone. Herein, a cooperative multi-player game-based optimization framework and an algorithm are presented to coordinate vehicles and achieve minimum values for the global pay-off conditions. Fuel consumption, passenger comfort, and travel time within the merging control zone were used as the pay-off conditions. After analyzing the characteristics of the merging control zone and selecting the appropriate control decision duration, multi-player games were decomposed into multiple two-player games. An optimal merging strategy was, thereby, derived from a pay-off matrix, and minimum payoffs were predicted for a number of different potential strategies. The optimal trajectory corresponding to the predicted minimum payoffs was then utilized as the control law to coordinate the vehicles merging. The proposed control scheme derives an optimal merging sequence and an optimal trajectory for each vehicle. The effectiveness of the proposed model is validated through simulation. The proposed controller is compared with two alternative methods to demonstrate its potential to reduce fuel consumption and travel time and to improve passenger comfort and traffic efficiency.

Improper handling of on-ramp merging may cause severe decrease of traffic efficiency and contribute to lower fuel economy, even increasing the collision risk. Cooperative control for connected and automated vehicles (CAVs) has the potential to significantly reduce the negative impact and improve safety and traffic efficiency. Implementation of cooperative on-ramp merging requires the assistance of the vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication, wherein the communication delay may cause negative impact on CAV cooperative control. In this paper, scenario of on-ramp merging for CAVs considering the V2I communication delay are studied. Statistical characteristics of the V2I communication delay are explored from both literature and real field test, and a communication delay estimation model based on statistical techniques are proposed. Specifically, we firstly model the CAV on-ramp merging scenario using optimal control in ideal situation. Then, several statistical characteristics of the V2I communication are investigated especially the probability density function of the V2I communication delay in several application scenarios. Further, we proposed a communication delay estimation model and used the modified vehicle state to compute the corresponding control law. Real field test of V2I communication delay indicated that distribution of V2I communication delay could correlate with the application scenario and normal distribution can be generally adopted to approximate the probability density function (PDF) when the number of samples is large enough. Numerical simulation of the CAV on-ramp merging scenario considering the V2I communication delay revealed that dynamic performance of the control process would be deteriorated impacted by the V2I communication delay and it might further impact the final control effect and lead to potential lateral collision in the merging area.

Curve is the traffic accident-prone area in the traffic system of the structural road. How to effectively detect the lane-line and timely give the traffic information ahead for drivers is a difficult point for the assisted safe driving. The traditional lane detection technology is not very applicable in the curved road conditions. Thus, a curve detection algorithm which is based on straight-curve model is proposed in this paper and this method has good applicability for most curve road conditions. First, the method divides the road image into the region of interest and the road background region by analyzing the basic characteristics of the road image. The region of interest is further divided into the straight region and the curve region. At the same time, the straight-curve mathematical model is established. The mathematical equation of the straight model is obtained by using the improved Hough transform. The polynomial curve model is established according to the continuity of the road lane-line and the tangent relationship between the straight model and the curve model. Then, the parameters of the curve model equation are solved by the curve fitting method. Finally, the detection and identification of the straight and the curve are realized respectively and the road lane-line is reconstructed. Experiments show that this method can accurately identify the curve lane-line, provide effective traffic information, make early warning and it also has a certain universality.

Connected and automated vehicles (CAVs) can improve traffic safety and transportation network efficiency while also reducing environmental impacts. However, congestion and accidents can easily occur at merging roadways. Therefore, coordinating cooperative merging of CAVs is one of the most common traffic management problems. This paper addresses the problem of integrated longitudinal and lateral cooperative merging control with practical implications for CAVs approaching on-ramps. A hierarchical and decentralized cooperative coordination framework was developed to systematically control the merging of CAVs. The control system of each vehicle can be divided into an upper-level and lower-level. For upper-level control, an optimal control-based algorithm considering input constraints was presented to optimize fuel consumption and passenger comfort. A decision strategy was developed to optimize the start time of lateral trajectory planning. To achieve lower-level control, a Proportional-Integral (PI) controller was used for tracking the optimized longitudinal speed of the upper-level and a decentralized unified algorithm based on nonlinear model predictive control was proposed for tracking the upper-level optimal trajectory. To avoid lateral collision, the driving safety field based on vehicle size and motion state was selected as one of tracking the optimization objectives. Efficiency of the proposed framework and the algorithm was validated by CarSim/Simulink co-simulations of near-real-world vehicle scenarios. The proposed integrated merging control system can improve traffic efficiency and reduce fuel consumption compared to baseline with the potential for real-world application. Furthermore, the results demonstrate the potential applicability of cooperative control methods based on upper-level vehicle control.

Ramp merging represents a bottleneck scenario that causes traffic congestion, accidents, and increases emissions. Connected and Automated Vehicles (CAVs) can realize the coordinated control of ramp merging through vehicle-to-infrastructure (V2I) for relieving above problems. Considering the previous studies on centralized ramp merging only involved single mainline, this paper proposes a multi-lane centralized collaborative control strategy using cooperative game. First, the merging rules of different lanes of vehicles in the merging area are defined, so that the vehicles can achieve the cooperative merging safely. Second, driving efficiency, comfort, and fuel consumption in the merging control zone are used as the cost function. The best merging sequences of vehicles in different lanes are solved by cooperative game. Finally, analytical solution of longitudinal optimal control for all vehicles is obtained by applying the Pontryagin principle. The effectiveness of the proposed method is verified through simulation under random traffic conditions. Compared to other centralized control algorithms, it can significantly improve driving efficiency and reduce fuel consumption. At the same time, the applicability of the method is verified by comparing with ExiD datasets, and some advantages are obtained in terms of fuel consumption.