Kernel-based Planning and Imitation Learning Control for Flow Smoothing in Mixed Autonomy Traffic

This article presents a new architecture for managing heterogeneous fleets aimed at achieving flow harmonization in mixed-autonomy traffic, demonstrating robustness across different sensing paradigms. We develop a kernel-based planning controller capable of providing anticipative coordination over low-bandwidth or high-latency networks. Furthermore, we employ a scenario-based optimization technique to tune the parameters of the proposed controller which offers performance improvement over the grid search technique across different simulation scenarios. Additionally, our architecture includes a local control strategy utilizing imitation learning, distinctively treating our kernel-based planning controller as the expert. This unique application bridges the gap between local sensing and global sensing by introducing input flexibility and vehicle control decentralization while preserving behavioral alignment with the expert’s potential actions. Our proposed architecture is shown to be adaptable across a broad spectrum of car platforms, accommodating vehicles with varying levels of sensing and actuation, highlighting its potential for widespread implementation in future transportation systems.