  Autonomous vehicle platoons present near- and long-term opportunities to
enhance operational efficiencies and save lives. The past 30 years have seen
rapid development in the autonomous driving space, enabling new technologies
that will alleviate the strain placed on human drivers and reduce vehicle
emissions. This paper introduces a testbed for evaluating and benchmarking
platooning algorithms on 1/10th scale vehicles with onboard sensors. To
demonstrate the testbed's utility, we evaluate three algorithms, linear
feedback and two variations of distributed model predictive control, and
compare their results on a typical platooning scenario where the lead vehicle
tracks a reference trajectory that changes speed multiple times. We validate
our algorithms in simulation to analyze the performance as the platoon size
increases, and find that the distributed model predictive control algorithms
outperform linear feedback on hardware and in simulation.
