  In this paper, we present the first experimental results of data-driven
predictive control for connected and autonomous vehicles (CAVs) in dissipating
traffic waves. In particular, we consider a recent strategy of Data-EnablEd
Predicted Leading Cruise Control (DeeP-LCC), which bypasses the need of
identifying the driving behaviors of surrounding vehicles and directly relies
on measurable traffic data to achieve safe and optimal CAV control in mixed
traffic. We present the implementation details of DeeP-LCC, including data
collection, equilibrium estimation, and control execution. Based on a miniature
experiment platform, we reproduce the phenomenon of stop-and-go waves in two
typical traffic scenarios: 1) open straight-road scenario under external
disturbances and 2) closed ring-road scenario with no bottlenecks. Our
experiments clearly demonstrate that DeeP-LCC enables one or a few CAVs to
dissipate the traffic waves in both traffic scenarios. These experimental
findings validate the great potential of DeeP-LCC in smoothing practical
traffic flow in the presence of noisy data, uncertain low-level vehicle
dynamics, and communication and computation delays. The code and videos of our
experimental results are available at https://github.com/soc-ucsd/DeeP-LCC.
