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。

评估自主编队算法的基准测试平台的设计与实现

Design and Realization of a Benchmarking Testbed for Evaluating  Autonomous Platooning Algorithms

In this paper we propose a parallel coordinate descent algorithm for solving
smooth convex optimization problems with separable constraints that may arise
e.g. in distributed model predictive control (MPC) for linear network systems.
Our algorithm is based on block coordinate descent updates in parallel and has
a very simple iteration. We prove (sub)linear rate of convergence for the new
algorithm under standard assumptions for smooth convex optimization. Further,
our algorithm uses local information and thus is suitable for distributed
implementations. Moreover, it has low iteration complexity, which makes it
appropriate for embedded control. An MPC scheme based on this new parallel
algorithm is derived, for which every subsystem in the network can compute
feasible and stabilizing control inputs using distributed and cheap
computations. For ensuring stability of the MPC scheme, we use a terminal cost
formulation derived from a distributed synthesis. Preliminary numerical tests
show better performance for our optimization algorithm than other existing
methods.

本文提出了一种并行坐标下降算法，用于解决具有可分离约束的光滑凸优化问题，在线性网络系统的分布式模型预测控制（MPC）中可能出现。我们的算法基于块坐标下降更新，并具有非常简单的迭代。我们证明了该算法在光滑凸优化的标准假设下具有（次）线性收敛率。此外，我们的算法使用本地信息，因此适用于分布式实现。此外，它具有较低的迭代复杂性，适用于嵌入式控制。我们派生了一个基于这种新并行算法的 MPC 方案，其中网络中的每个子系统都可以使用分布式和廉价的计算来计算可行的和稳定的控制输入。为了确保 MPC 方案的稳定性，我们使用从分布式合成中派生的终端成本公式。初步的数字测试显示我们的优化算法比其他现有方法具有更好的性能。