Data-driven Learning and Control - DDLC

This talk summarizes recent rapid progress in integrating machine learning techniques with cloud computing and automation in the context of mixed-autonomy (i.e. contexts in which humans interact with machines). The results are developed in the context of traffic automation. We present a new platform in which a cloud-based system broadcasts high-level “speed plans” (generated through optimal control and neural approximations of hyperbolic partial differential equations) via lossy communication channels such as the cellular network to a fleet of vehicles with a given level of automation. The vehicles, using their local automation stack, run deep reinforcement learning algorithms collaboratively to control surrounding traffic. The algorithms and the platform are designed to smooth “stop-and-go” waves on freeways, which are a significant cause of energy waste and accidents. Results from a large-scale experiment involving 100 automated vehicles are presented, in which Nissan, Toyota, and GM vehicles collectively run the algorithms on the I-24 freeway in Nashville, TN, demonstrating up to a 10% improvement in overall energy consumption.