This paper proposes a distributed nonlinear consensus delay-dependent control algorithm for a connected vehicle (CV) platoon. In particular, considering that the behavior of the following vehicle is associated with the longitudinal inter-vehicle gap with respect to the preceding vehicle, a nonlinear function is designed to characterize the car-following interactions between CVs. Then, a nonlinear consensus algorithm is proposed by incorporating the car-following interactions and heterogeneous time delays. The delay-dependent convergence condition of the proposed control algorithm is analyzed using the Lyapunov-Krasovskii method, and an estimate of the delay bound is provided. Under the proposed algorithm, not only can the consensus of CVs be guaranteed but also the behavior of vehicles is consistent with traffic flow theory. Finally, an example using a 10-vehicle platoon is provided under three scenarios: no time delays, heterogeneous time delays, and homogeneous time delays. Results from extensive simulations verify the effectiveness of the proposed control algorithm in terms of the position, velocity, and acceleration/deceleration profiles.

In a mixed traffic environment of human and autonomous driving, it is crucial for an autonomous vehicle to predict the lane change intentions and trajectories of vehicles that pose a risk to it. However, due to the uncertainty of human intentions, accurately predicting lane change intentions and trajectories is a great challenge. Therefore, this paper aims to establish the connection between intentions and trajectories and propose a dual Transformer model for the target vehicle. The dual Transformer model contains a lane change intention prediction model and a trajectory prediction model. The lane change intention prediction model is able to extract social correlations in terms of vehicle states and outputs an intention probability vector. The trajectory prediction model fuses the intention probability vector, which enables it to obtain prior knowledge. For the intention prediction model, the accuracy can be improved by designing the multi-head attention. For the trajectory prediction model, the performance can be optimized by incorporating intention probability vectors and adding the LSTM. Verified on NGSIM and highD datasets, the experimental results show that this model has encouraging accuracy. Compared with the model without intention probability vectors, the impact of the model on NGSIM dataset and highD dataset in RMSE is improved by 57.27% and 58.70% respectively. Compared with two existed models, evaluation metrics of the intention prediction can be improved by 7.40-10.09% on NGSIM dataset and 2.17-2.69% on highD dataset within advanced prediction time 1s. This method provides the insights for designing advanced perceptual systems for autonomous vehicles.

This paper focuses on platoon control of multi-vehicle systems in a realistic vehicle-to-vehicle/vehicle-to-infrastructure (V2V/V2I, or V2X) communication environment. To this end, the communication probability of the beacon transmission is analyzed based on the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism. Then, a new car-following model is proposed by considering the communication probability to capture the car-following behavior of connected vehicles (CVs) equipped with V2X communications. The stability of the proposed car-following model is analyzed using the perturbation method. In addition, a nonlinear consensus-based longitudinal control algorithm is designed by considering the interactions between CVs, and an artificial function-based lateral control algorithm is presented. The convergence of the longitudinal and lateral control algorithms is analyzed using the Hurwitz stable theorem and Lyapunov technique, respectively. Also, the string stability of the vehicular platoon is performed based on the infinity-norm method. Finally, field experiments are conducted using four CVs under the scenarios of platoon forming, vehicle merging, and vehicle diverging. The results verify the effectiveness of the proposed method in terms of the trajectory and velocity profiles.

Vehicular platoon control can effectively achieve group consensus, improve vehicular running safety and increase road capacity. However, some constraints exist in practical situations due to the limitations of traffic environment in time-varying metrics (time-delay, packet-dropout or interruption) in wireless communication systems. In this work, a distributed model predictive control (MPC) algorithm is proposed for connected vehicle platoon with a focus on switching communication topologies and control strategy under abnormal communications. Firstly, the predecessor-leader following is selected as the basic communication topology, by which the switching communication topology and the desired vehicle spacing policy are established. Secondly, the platoon control algorithm of connected vehicles is established and a set of constraints is analyzed. Thirdly, the ${\mathcal{ L}}_{2} $ -norm string stability criterion and the asymptotic stability criterion are considered within the proposed MPC. Finally, a co-simulation platform for connected vehicle platoon is developed based on Prescan/Matlab/V2X communication simulator. In addition, the platoon control algorithm is tested in three traffic scenarios including normal communication, leading vehicle with abnormal communication and following vehicle with abnormal communication. The experiments demonstrate that the communication topologies in different communication environments can be switched well in real time through the proposed platoon control algorithm. In addition, the string stability, the consistency of vehicle spacing, speed and acceleration are proven to be guaranteed simultaneously.

This paper proposes a longitudinal platoon controller for connected vehicles (CVs) by considering the information of multiple preceding vehicles and the car-following interactions between CVs. The stability of the proposed controller is analyzed using the Routh criterion. For the verification, we develop an integrated platoon control framework for CVs in a V2V/V2I communication environment. The proposed framework consists of two main components: simulation platform and experimental platform. In particular, the simulation platform is developed based on the TransModeler software, and the experimental platform is designed using the self-developed V2X devices. Finally, a scenario of platoon forming is taken as an example and is conducted in simulation platform and experimental platform, respectively. Results demonstrate the effectiveness of the proposed controller with respect to the trajectory and velocity profiles.