Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving
Model predictive control (MPC) using randomized optimization is expected to solve different control problems. However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the au...
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| Veröffentlicht in: | IEEE transactions on intelligent vehicles 2022-03, Vol.7 (1), p.11-20 |
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| creator | Muraleedharan, Arun Okuda, Hiroyuki Suzuki, Tatsuya |
| description | Model predictive control (MPC) using randomized optimization is expected to solve different control problems. However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the autonomous driving using a radio-controlled (RC) car. First of all, a sample generation technique in the frequency domain is discussed. This prevents undesirable randomness which affect the smoothness of the steering operation. Second, the proposed randomized MPC is implemented on a Graphics Processing Unit (GPU). The expected GPU acceleration in calculation speed at various problem sizes is also presented. The results show the improved control performance and computational speed that was not achievable using CPU based implementation. Besides, the selection of parameters for randomized MPC is discussed. The usefulness of the proposed scheme is demonstrated by both simulation and experiments. In the experiments, a 1/10 model RC car is used for collision avoidance task by autonomous driving. |
| doi_str_mv | 10.1109/TIV.2021.3062730 |
| format | Article |
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However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the autonomous driving using a radio-controlled (RC) car. First of all, a sample generation technique in the frequency domain is discussed. This prevents undesirable randomness which affect the smoothness of the steering operation. Second, the proposed randomized MPC is implemented on a Graphics Processing Unit (GPU). The expected GPU acceleration in calculation speed at various problem sizes is also presented. The results show the improved control performance and computational speed that was not achievable using CPU based implementation. Besides, the selection of parameters for randomized MPC is discussed. The usefulness of the proposed scheme is demonstrated by both simulation and experiments. 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In the experiments, a 1/10 model RC car is used for collision avoidance task by autonomous driving.</description><subject>Acceleration</subject><subject>Automobiles</subject><subject>Autonomous vehicles</subject><subject>Collision avoidance</subject><subject>Computational modeling</subject><subject>Frequency-domain analysis</subject><subject>graphics processing unit (GPU)</subject><subject>Graphics processing units</subject><subject>model predictive control</subject><subject>Optimization</subject><subject>Predictive control</subject><subject>Radio control</subject><subject>Real-time systems</subject><subject>sampling based optimization</subject><subject>Smoothness</subject><subject>Steering</subject><subject>Vehicle dynamics</subject><issn>2379-8858</issn><issn>2379-8904</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWGrvgpeA562TjyabY6lfhYpSaq9LuplIyu6mZncF_fW2tAoD7xyedwYeQq4ZjBkDc7ear8ccOBsLUFwLOCMDLrTJcgPy_G_PJ_klGbXtFgCYynkOZkDWS7RVtgo10nm9q7DGprNdiA2Nni5t42IdftDRl-iwom8JXSi78IV0FpsuxYr6mOi072IT69i39D6Fr9B8XJELb6sWR6cckvfHh9XsOVu8Ps1n00VWCmO6TFlQwuXSWaW55NrkGrHcgLfCIRPMSiilNBJgo-UEjfLaOy-kKNEZw1AMye3x7i7Fzx7brtjGPjX7lwVXEyE054LvKThSZYptm9AXuxRqm74LBsVBYLEXWBwEFieB-8rNsRIQ8R83QqnD_AJthGwM</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Muraleedharan, Arun</creator><creator>Okuda, Hiroyuki</creator><creator>Suzuki, Tatsuya</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the autonomous driving using a radio-controlled (RC) car. First of all, a sample generation technique in the frequency domain is discussed. This prevents undesirable randomness which affect the smoothness of the steering operation. Second, the proposed randomized MPC is implemented on a Graphics Processing Unit (GPU). The expected GPU acceleration in calculation speed at various problem sizes is also presented. The results show the improved control performance and computational speed that was not achievable using CPU based implementation. Besides, the selection of parameters for randomized MPC is discussed. The usefulness of the proposed scheme is demonstrated by both simulation and experiments. 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| identifier | ISSN: 2379-8858 |
| ispartof | IEEE transactions on intelligent vehicles, 2022-03, Vol.7 (1), p.11-20 |
| issn | 2379-8858 2379-8904 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Acceleration Automobiles Autonomous vehicles Collision avoidance Computational modeling Frequency-domain analysis graphics processing unit (GPU) Graphics processing units model predictive control Optimization Predictive control Radio control Real-time systems sampling based optimization Smoothness Steering Vehicle dynamics |
| title | Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving |
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