Signalized intersections play an important role in transportation efficiency and vehicle fuel economy in urban areas. This paper proposes a cooperative method of traffic signal control and vehicle speed optimization for connected automated vehicles, which optimizes the traffic signal timing and vehicles' speed trajectories at the same time. The method consists of two levels, i.e., roadside traffic signal optimization and onboard vehicle speed control. The former calculates the optimal traffic signal timing and vehicles' arrival time to minimize the total travel time of all vehicles; the latter optimizes the engine power and brake force to minimize the fuel consumption of individual vehicles. The enumeration method and the pseudospectral method are applied in roadside and onboard optimization, respectively. Simulation studies are conducted to compare the proposed method with benchmark methods. The results show significant improvement of transportation efficiency and fuel economy by the cooperation method.

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

The emerging connected automated vehicle (CAV) technique brings a new opportunity for the development of cooperative collision avoidance systems. This paper proposes a distributed coordinated brake control (DCBC) algorithm for longitudinal collision avoidance of multiple CAVs. Firstly, the studied scenario is defined, within which the communication topology and vehicle dynamics are modeled for a vehicle string and the control objective is designed to minimize the total relative kinetic energy. Next, the control problem is formulated into a centralized model predictive control (MPC) problem, which is then transformed into a quadratic programming (QP) problem. By separating the QP problem into several sub-problems assigned to each CAV, distributed optimization is introduced to solve the QP problem in a distributed manner. Elaborate numerical simulation comparisons are conducted to demonstrate the advantage of the proposed DCBC algorithm.

This work deals with the Multi-Vehicle Trajectory Planning (MVTP) problem in unstructured conflict areas. Compared with MVTP on structured roads, the more complex intersection and the more conflicting motions of vehicles, set higher requirements for the real-time of the planner. To address the issue, this work proposes a centralized decision-making distributed planning framework, to generate trajectories for multiple vehicles navigating conflict areas on unstructured roads. In the multi-vehicle collaboration process, vehicles are assigned a priority upon entering the control area, with higher-priority vehicles treated as dynamic obstacles to be avoided. The distributed planning is divided into three stages: 1) trajectory search utilizing Velocity-Hybrid A* (V-Hybrid A*), 2) path optimization with respect to both dynamic and static obstacles, and 3) speed optimization leveraging the convex space created by the initial solution. In the trajectory search of individual vehicles, each expansion node of V-Hybrid A* is given an acceleration to get a coarse trajectory with discrete velocity. Moreover, a box constraint is established to optimize the path of the coarse trajectory. Then, according to the homotopy class provided by the coarse trajectory, speed optimization is carried out to obtain the final single vehicle trajectory. In our simulation experiments, we randomized the generation of numerous vehicles assigned with varied tasks to evaluate the algorithm’s effectiveness. Ultimately, real-vehicle experiments substantiated the algorithm’s practical feasibility. The results indicate that the proposed method can significantly improve the traffic efficiency in conflict areas and has practical applicability.

The complex slip characteristics between the tracks and the terrain make it difficult to build an accurate model for intelligent tracked vehicles. Both the path planning and lateral control, however, are highly dependent on the accurate tracked vehicle model. To overcome the issue, a slip model based on the instantaneous centers of rotation (ICRs) of tracks and a dual-layer adaptive unscented Kalman filter (DAUKF) are used to estimate the ICR locations in real-time without requiring prior knowledge of terrain parameters. First, the historical trajectory information is used to estimate ICR locations by the upper-layer AUKF estimator preliminarily. The ICR locations estimated in the upper-layer and the current vehicle state are then imported into the model to predict the future trajectory which can be used to estimate the offset as compensation of preliminarily estimated ICR locations by the lower-layer AUKF estimator. The proposed DAUKF is verified by simulations on MATLAB/Simulink. In order to further verify the effectiveness and practicability of the algorithm, a large number of experiments under different terrains and road conditions are implemented on the electric-drive tracked vehicle. The simulation and experimental results illustrate that the proposed DAUKF can estimate the ICR locations efficiently and accurately, which can improve the accuracy of the tracked vehicle model compared with those of extended Kalman filter (EKF), UKF, and AUKF.

Road network control is challenging but critical in enhancing urban traffic. This paper proposes a management method for a road network with non-signalized intersections in the connected-vehicle environment, which coordinates connected and automated vehicles’ movements to make the non-signalized intersections conflict-free and efficient. Firstly, the proposed method projects vehicles in road networks into virtual platoons and builds the road-network-wide conflict-free geometric topology considering the vehicles’ conflicting relationships, which describes the geometry car-following relationship in virtual platoons. Then, a distributed linear controller is designed considering vehicle dynamics and communication topology to organize vehicles’ movements with the desired geometric topology. Finally, simulations are conducted to verify the proposed method with different traffic demands. Simulation results show the proposed method can significantly improve traffic efficiency, as well as traffic safety.