With the development of intelligent vehicle and platoon technology, multi-platoon system will become a new solution to further improve traffic efficiency on highways. However, the existing research seldom consider the interference of human-driven vehicles on multi-platoon stability and the following strategy of multi-platoon leader in mixed traffic. In this paper, a robust distributed model predictive control method for multi-platoon leader in mixed traffic is proposed to reduce the impact of human-driven vehicles on multi-platoon control performance. The following control strategy of multi-platoon leader is proposed firstly, which flexibly determines the following control targets according to the states of leader and HDV to avoid unnecessary frequent acceleration and deceleration. Then, the robust model prediction controller of multi-platoon leader is designed, where the states of sub-platoon leader are added to the objective function in the nominal system optimization problem to reduce the states change of the following vehicles under the influence of HDV from both forward and backward traffic. Furthermore, the auxiliary control law is designed to eliminate the error between the actual states and the nominal states to achieve the suppression of HDV interference. The simulation results show that the multi-platoon leader following control strategy can effectively reduce the speed variation of the multi-platoon to suppress the impact of HDV motion uncertainty on multi-platoon. Moreover, compared with the robust model prediction method of single-platoon leader without considering the state of the rear vehicle, the proposed method can reduce the control errors and improve the stability of multi-platoon.

This article aims to address the unnecessary stopping and low efficiency issues present in existing multi-machine cooperative steering control methods. To tackle this challenge, a novel cooperative control approach for multiple agricultural machines is proposed, considering two typical steering modes of farm machinery. This approach encompasses a multi-machine cooperative control framework suitable for both steering modes. Based on the established lateral and longitudinal kinematics models of the farm machines, the method includes a path-tracking controller designed using the pure pursuit and Stanley algorithms, a formation-keeping controller based on PID control, and a T-turn cooperative-steering controller based on a problem-solving approach. To assess the method’s viability, a collaborative simulation platform utilizing CarSim and Simulink was constructed, which conducted simulations for both U-turn and T-turn cooperative steering controls. The simulation results indicate that the proposed control framework and methodology can effectively ensure no collision risk during the U-turn and T-turn cooperative steering processes for three farm machines, eliminating stopping in T-turn, enhancing safety, and improving fuel economy. Compared with traditional sequential control methods, the proposed approach reduced operation time by 17.47 s and increased efficiency by 15.29% in the same scenarios.

Failures in vehicle-to-vehicle (V2V) communication and millimeter-wave radar affect connected vehicle platoon operation. This study links an ISO 26262-oriented preliminary risk assessment and Hazard and Operability Study (HAZOP) to role-specific degraded control. Long-distance adaptive cruise control (ACC), variable-speed cruise control (CC), and fixed-speed CC are selected according to the remaining information. Evaluation combines a 16-condition PreScan-Simulink matrix, represented by 12 trajectory files with shared communication-case results, and electric micro-vehicle tests under externally supplied fault labels. Conditional gap and delay inequalities describe forward-clearance requirements under specified motion bounds. The records demonstrate mode switching and spacing responses under the tested conditions, with spacing-target shortfalls in simultaneous-loss bend cases. Each 20 m/s straight-road file contains an unresolved Vehicle 3 acceleration sample near −8.94 m/s² at 20.01 s, and near-identical Vehicle 2 trajectories limit comparison between two cases. The supplied records support output reanalysis; complete controller reproduction and trial-level uncertainty remain unresolved. The results support implementation feasibility at the supervisory-control layer, with safety validation requiring verified motion bounds and end-to-end fault detection.