This will be a hybrid event with in-person attendance in Levine 307 and virtual attendance on Zoom.

ABSTRACT

The ever-increasing penetration of level-2 autonomous vehicles (AVs) offers an opportunity to reshape the energy efficiency and throughput of our highways. Even at current low penetration rates (1-5%), we have observed in small settings that adopting different driving behaviors from humans can sharply decrease fuel consumption by eliminating ubiquitous stop-and-go waves from traffic. We examined this idea at scale, showing that we can use reinforcement learning to design AV behaviors that operate cooperatively to smooth traffic in large, realistic simulators. We performed a large-scale road test, the first of its kind, in which we deployed a hundred of these cruise controllers onto a highway to show traffic smoothing at scale. Finally, we discuss ongoing efforts to benchmark and test autonomous vehicles by building fast simulators populated by RL agents modeling human behaviors.