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.

自主驾驶车队展现中短期机会以提高运营效率和挽救生命。本文介绍了一个可以评估和基准测试装载传感器的 1/10 比例车辆车队算法的测试平台，通过对典型车队场景进行变速的参考轨迹跟踪的实验，评估了线性反馈和两种分布式模型预测控制算法，并验证了算法的性能随车队规模增加而增加，结果表明分布式模型预测控制算法在硬件和仿真中优于线性反馈。