Uncertainty is a major concern in vehicle path tracking control design. The coefficients of the uncertainty bound are unknown. They are assumed to lie within prescribed fuzzy sets. First, based on the path tracking kinematic model, this article innovatively formulates the vehicle path tracking task as a constraint-following problem. Second, we put forward a deterministic adaptive robust control law with a tunable parameter to ensure the uniform boundedness and ultimate uniform boundedness of the closed-loop system. Third, an optimal scheme for the tunable parameter is proposed based on the fuzzy uncertainty. The resulting optimal robust control (ORC) minimizes a comprehensive fuzzy performance index that involves the fuzzy system performance and the control cost. The results of the CarSim-Simulink cosimulation and the hardware-in-loop experiment together show that the proposed ORC exhibits a superior path tracking performance.

To improve the reliability of the shared steering control system, and weaken the influence of uncertain driver behavior on driving safety, an extended shared steering control system with an authority adaptive allocation model is proposed. The system framework consists of upper-level and lower-level. The upper-level is an adaptive authority allocation model, which is composed of a reference model and an authority dynamic allocation model. The reference authority level can be calculated by the reference model, which is used as the reference of the authority dynamic allocation model. The authority levels of the autonomous controller and driver are gained by the adaptive authority allocation model. The lower level is the human-machine shared steering control, which includes the driver and autonomous controller. Using the authority level obtained by the upper-level, the lower-level can realize the coordinated control of human and machine. The effectiveness of the proposed approach is verified by simulation. The results show that relative to the numerical function model, the proposed approach can effectively weaken the impact of driver misoperation and poor preview time, the trajectory tracking ability and handling stability are improved significantly, and the human-machine conflict and the workload of the controller are weakened.

Deep deterministic policy gradient (DDPG)-based car-following strategy can break through the constraints of the differential equation model due to the ability of exploration on complex environments. However, the car-following performance of DDPG is usually degraded by unreasonable reward function design, insufficient training, and low sampling efficiency. In order to solve this kind of problem, a hybrid car-following strategy based on DDPG and cooperative adaptive cruise control (CACC) is proposed. First, the car-following process is modeled as the Markov decision process to calculate CACC and DDPG simultaneously at each frame. Given a current state, two actions are obtained from CACC and DDPG, respectively. Then, an optimal action, corresponding to the one offering a larger reward, is chosen as the output of the hybrid strategy. Meanwhile, a rule is designed to ensure that the change rate of acceleration is smaller than the desired value. Therefore, the proposed strategy not only guarantees the basic performance of car-following through CACC but also makes full use of the advantages of exploration on complex environments via DDPG. Finally, simulation results show that the car-following performance of the proposed strategy is improved compared with that of DDPG and CACC. Note to Practitioners—This article presents a new car-following strategy, which avoids the impact of deep deterministic policy gradient (DDPG) performance degradation on the system. In the proposed strategy, DDPG is replaced with cooperative adaptive cruise control (CACC) when the performance of DDPG is worse than that of CACC. Meanwhile, a switching rule is designed to guarantee that the change rate of acceleration is smaller than the threshold. Simulation results show that the performance of hybrid car-following strategy has been improved compared with that of only using CACC or DDPG. Moreover, the proposed strategy has the advantages of low computational burden, high real-time performance, and good scalability.

This paper presents a robust path tracking control method by utilizing the ideology of constraint-following approach for uncertain underactuated autonomous vehicles. The uncertainties are bounded with an unknown boundary. They do not all fall within the range space of the input matrix. Based on kinematic relations between the desired path and vehicle, the path tracking task is transformed into an equality constraint of the vehicle lateral dynamics states. The control goal is to make the underactuated vehicle follow the constraint, thus realizing the desired tracking performance. The constraint-following robust control (CFRC) is designed in two steps. First, a servo control design for the nominal system is devised without considering uncertainty and initial constraint deviation. Second, the uncertainty is meticulously decomposed into matched and mismatched portions based on the geometric structural characteristics of the constraint dynamics system. As a result, since the mismatched uncertainty is orthogonal to the constraint-following geometric space, it “disappears” in the stability analysis. The matched uncertainty is estimated by a self-adjusting leakage type adaptive law. On this basis, a robust control is designed based on the estimated matched uncertainty. Through Lyapunov minimax analysis, the proposed control method guarantees the approximate constraint-following performance. Finally, the TruckSim–Simulink co-simulations and real vehicle experiments are presented. The results show that the proposed control can robustly realize excellent tracking performance in the presence of time-vary uncertainties.

Cooperative on-ramp merging control for connected and automated vehicles have been widely studied in recent years. One of the major challenges for this multi-vehicle coordination problem is to execute a safe and efficient merging process considering realistic uncertainties in vehicle dynamics. This paper puts forward a two-layer merging control framework made up of centralized sequencing and distributed control. A Roadside Agent installed in the merging area identifies the merging sequence of vehicles approaching the merging area in terms of their estimated time of arrival at the merging point. Once the sequence is determined, distributed controllers will guide the vehicles to execute the merging process. The controller is designed based on Udwadia-Kalaba approach and Lyapunov stability theory and is capable of handling nonlinear vehicle dynamics and time-varying uncertainty. It renders both uniform boundedness and uniform ultimate boundedness performance for the spacing error and thus executes the merging process effectively. Simulations results verify the superiority of the proposed control method compared with baselines methods. Furthermore, the effectiveness of the proposed method is also validated under different traffic demands and multiple-vehicle-pack merging situation.