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.

In order to improve the trajectory tracking accuracy and vehicle lateral stability, the paper proposes a lateral and longitudinal dynamics control framework of autonomous vehicles considering multi-parameter joint estimation. First, the multi-parameter joint estimation based on adaptive unscented Kalman filter (AUKF) is constructed to decouple and estimate the longitudinal position of vehicle's center of gravity (CG), tire-road friction coefficient (TRFC), tire cornering stiffness (TCS), tire vertical force (TVF) and road grade. Then, focusing on the large lateral acceleration condition, a lateral control based on the optimal front-tire lateral force is proposed by constructing the linear quadratic regulator (LQR) and combining the multi-parameter joint estimation. Additionally, a longitudinal control based on the drive and brake force compensation to realize the accurate speed tracking by combining road slop estimation is achieved. The fuzzy system employs the drive and brake force deviation as the input to implement the compensation of the throttle and brake. The proposed estimation and control framework are verified by co-simulation on PreScan and CarSim preliminarily. In order to further verify the effectiveness and practicability of algorithm, experiments are implemented on an autonomous vehicle platform, a hybrid Lincoln MKZ. Simulation and experimental results show that the proposed estimation and control framework possess excellent performance and enhance the tracking accuracy and lateral stability.

Cooperation among multiple connected vehicles (CVs) brings swarm intelligence to our transportation systems and helps improve their performance. This study proposes a fuel economy optimization approach for a platooning vehicle swarm via distributed economic model predictive control (DEMPC). Within the DEMPC framework, each CV shares its assumed trajectory in the predictive horizon with its neighboring CVs at each control loop. With its neighbors’ and its own assumed trajectories, each CV first solves an open-loop control optimization problem for platoon formation, and then solves an open-loop economic optimization problem for direct fuel economy improvement. In particular, the optimal cost of the former optimization problem is used in the latter one to build an upperbound constraint for stability guarantee. The asymptotic convergence of assumed terminal states is given in the analysis, and the recursive feasibility of the two optimization problems are proved with an explicit constraint on the weight matrices in the open-loop control optimization problem. Based on these analyses, the asymptotic stability of the closed-loop system is finally proved through Lyapunov analysis. Numerical simulation results validate the effectiveness of the proposed approach in terms of closed-loop stability and fuel economy improvement. Note to Practitioners—This work aims to optimize the fuel economy for a swarm of vehicles running in a platoon. Existing approaches on platoon control mainly focus on the accurate tracking of the following vehicles, but this may cause aggressive control and hence lead to poor fuel economy. Therefore, this paper proposes a distributed economic model predictive control approach to explicitly optimize the fuel consumption rate of each vehicle. The proposed method can improve fuel economy and reduce communication requirements and burdens for platooning vehicle swarms. The paper assumes no communication time delay and packet drops, so the impact of unreliable communication will be studied in the future work.

The connected and automated vehicle (CAV) technique is a critical application of intelligent vehicles (IV) and is gaining widespread attention for its prospects of increasing driving efficiency. This paper investigates longitudinal control of vehicle platoons subject to both mismatched velocity disturbance and matched acceleration disturbance. The mismatched and matched disturbances are estimated by a finite-time disturbance observer (DO). Furthermore, two coupled sliding mode platoon controllers are proposed incorporating observed disturbances for longitudinal cooperation of platoons. By using and extending the coupled sliding mode technique, not only closed-loop stability but also string stability is finally proved under bidirectional (BD) and predecessor-following (PF) topologies, respectively. Simulations and an experiment are conducted to validate not only the theoretical effectiveness but also the practical performance of the proposed controllers.

Computation offloading is considered as a promising method to improve computation performance of Intelligent Vehicles (IVs), where IVs can offload resource-hungry applications to Mobile Edge Computing (MEC) servers. However, due to the dynamic environment of Intelligent Transportation Systems, it's challenging to develop an effective offloading scheme that ensures stable operation of the system. Furthermore, IVs only offload tasks to MEC servers or parked vehicles, how to take full advantage of resources in a random traffic flow is a main challenge. In this paper, we propose a vehicular-cloud-assisted MEC network in urban cities to fully explore the idle resources and ensure the stability of task queue. Specifically, we develop a novel virtual platform based on deep neural network that predicts vehicles trajectory and creates vehicular cloud to integrate computation resources. We then design a vehicular-cloud-assisted offloading scheme that aims to maximize task throughput in continuous time slots subject to multi-constraint. To address the coupling among multiple variables and different time slots, we put forward a lightweight framework to apply the Lyapunov optimization in combination with deep reinforcement learning. In particular, we apply Lyapunov drift-plus-penalty approach to decompose the initial problem into per-slot subproblems and control the long-term task queue stability. To further reduce the computation complexity, we decouple the subproblem into three processes, and the deep Q-network is put forward to solve it. Extensive simulations demonstrate that under different task arrivals, our proposed scheme can achieve the task queue stability within 10 time slots with a low transmission power.