自律プラトゥーニングアルゴリズム評価のためのベンチマークテストベッドの設計と実現
Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms
1/10スケール車両とオンボードセンサを用いたプラトゥーニングアルゴリズム評価用テストベッドを構築し、線形フィードバックと分散モデル予測制御の2手法を比較評価した。
著者: Michael Shaham, Risha Ranjan, Engin Kirda, Taskin Padir
分類: cs.RO, cs.AI, cs.MA, cs.SY, eess.SY, math.OC
原文アブストラクト
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.
