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.

翻译：自动驾驶车辆编队在近期和长期均具有提升运营效率及挽救生命的潜力。过去三十年，自动驾驶领域快速发展，催生了能够减轻人类驾驶员负担并减少车辆排放的新技术。本文介绍了一个基于1/10比例车辆与车载传感器的编队算法评估与基准测试平台。为展示该平台的实用性，我们评估了三种算法（线性反馈算法与两种分布式模型预测控制变体），并在典型的编队场景（头车跟踪一条多次变速的参考轨迹）中比较其性能。我们通过仿真验证了算法在编队规模增大时的表现，发现分布式模型预测控制算法在硬件实验和仿真中均优于线性反馈算法。