Hybrid Approach for Stabilizing Large Time Delays in Cooperative Adaptive Cruise Control with Reduced Performance Penalties

Cooperative adaptive cruise control (CACC) is a smart transportation solution that can mitigate traffic jams and improve road safety. CACC performance is heavily impacted by communication time delay; moreover, control theory solutions generally compromise control performance by tuning control gains in order to maintain plant stability. We propose a control-machine learning hybrid approach called deep time delay filter (DTDF). DTDF predicts the present (un-delayed) car states given time delayed versions. We successfully train a neural network for the DTDF method and use a physical testbed to show that DTDF can mitigate the effects of constant time delays as large as 5s while maintaining superior control performance compared to that of a baseline control algorithm.