The cooperation between connected and automated vehicles (CAVs) has emerged as a promising way to improve traffic efficiency and safety for ramp merging on highways. Existing research mostly focused on the collaboration of individual CAVs, while the cooperative merging strategy in mixed traffic considering vehicle platoons has been less explored. To address the above problem, this study aims to build connections between sequence scheduling and motion planning in mixed traffic, where individual CAVs, CAVs platoons, and mixed platoons coexist. First, optimal control strategies are presented for vehicles with flexible merging points based on Pontryagin’s minimum principle (PMP), and the final vehicle states in variable conditions are summarized in a phase diagram. Subsequently, the optimal final-state phase diagram is introduced into the passing sequence tree search process, which is designed for different vehicle groups in mixed traffic. Heuristic pruning rules are added to the depth-first search strategy to facilitate finding the optimal solution. Finally, an event-triggered receding horizon optimization algorithm is developed for continuous implementation. The numerical simulations are conducted in multiple traffic volumes, and the simulation results reveal that our proposed algorithm significantly improves the overall traffic efficiency and reduces vehicle-passing delays compared with the traditional FIFO-based cooperation method.

For current power supply systems in internal combustion engine (ICE) vehicles, there exists a lack of comprehensive monitoring and sufficient management. There is no effective fault diagnosis and fault-tolerant protection either. With increasing number of devices applied in ICE vehicles, power supply system of vehicle is in desperate need of improvement. In this study, a novel in-vehicle intelligent electric power supply network with comprehensive on-board fault diagnosis and real-time fault-tolerant protection is proposed. Firstly, the architecture of in-vehicle intelligent electric power supply network is established, making the power supply process of all devices smarter. Then, a hybrid-signal-based method is presented for on-board detection of multifarious faults, and over-temperature failure of wiring harness is detected based on the proposed real-time temperature evaluation model of wiring harness. Furthermore, a multi-level fault-tolerant protection method is proposed based on redundant design, to achieve power supply security and avoid false protection. Finally, tests are performed on the vehicle as well as test bench to verify the feasibility and reliability of the proposed power supply network. The results reveal that each device's power supply process is appropriately monitored, online detection of faults is successfully achieved, and the fault-tolerant method is able to realize remediation and protection in real time.

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.

Intelligent connected vehicles (ICVs) technologies will bring significant changes to future transportation, and urban intersections will be an important scenario for the application of ICVs. There exists one significant challenge to address for the control of ICVs in unsignalized, multi-intersection road networks, that is, how to realize the comprehensive optimization of traffic efficiency and energy saving. To solve this problem, the distributed and hierarchical optimal control architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the distributed model predictive control (DMPC) method is utilized for distributed optimization control of multi-intersection road network systems, to achieve optimization in terms of traffic efficiency. For the vehicle control layer, based on the reference speed optimized from the cloud decision layer, the DMPC method is further utilized for distributed optimal control of each vehicle platoon, to achieve optimization in terms of energy saving. Finally, the comparative simulation tests are carried out based on MATLAB and SUMO. The feasibility and effectiveness of the proposed method were verified, and the improvement of traffic efficiency and energy saving was achieved.