Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910650081476608 |
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| author | Shaham, Michael Ranjan, Risha Kirda, Engin Padir, Taskin |
| author_facet | Shaham, Michael Ranjan, Risha Kirda, Engin Padir, Taskin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09233 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms Shaham, Michael Ranjan, Risha Kirda, Engin Padir, Taskin Robotics Artificial Intelligence Multiagent Systems Systems and Control Optimization and Control 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. |
| title | Design and Realization of a Benchmarking Testbed for Evaluating Autonomous Platooning Algorithms |
| topic | Robotics Artificial Intelligence Multiagent Systems Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2402.09233 |