Knowledge-guided self-learning control strategy for mixed vehicle platoons with delays

As autonomous vehicles and traditional vehicles will coexist for several decades, how to efficiently manage the mixed traffic, while enhancing road throughput, fuel consumption and traffic stability becomes a challenge. This is due to the randomness and heterogeneity of traditional vehicles interspersed among autonomous vehicles. Moreover, communication delays arising from the shared wireless communication network substantially degrade the performance of platooning control for connected autonomous vehicles. To address these challenging problems, this paper proposes a knowledge-guided self-learning mixed platoon control strategy. Firstly, the proposed strategy extracts key features of the continuous and aggregated behavior of traditional vehicles, such as desired time-varying time gap and standstill spacing, by integrating knowledge from the kinematic wave model and Newell's car-following model. This helps autonomous vehicles predict traditional vehicles' trajectories. Secondly, to tackle delayed current state information, the study incorporates previous control instructions into the state representation of the soft actor-critic algorithm. Simulations show the proposed strategy outperforms existing methods in traffic stability, passenger comfort, energy consumption cost and traffic oscillation dampening, with a zero collision rate in vehicle merging and diverging scenarios. The framework provides a generalizable and scalable solution for the development and adoption of connected autonomous vehicle systems.