Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration
Applying reinforcement learning algorithms to autonomous driving is difficult because of mismatches between the simulation in which the algorithm was trained and the real world. To address this problem, data from global navigation satellite systems and inertial navigation systems (GNSS/INS) were use...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2022/9916292 |
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| _version_ | 1849308890217316352 |
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| author | Jiseong Heo Hyoung woo Lim |
| author_facet | Jiseong Heo Hyoung woo Lim |
| author_sort | Jiseong Heo |
| collection | DOAJ |
| description | Applying reinforcement learning algorithms to autonomous driving is difficult because of mismatches between the simulation in which the algorithm was trained and the real world. To address this problem, data from global navigation satellite systems and inertial navigation systems (GNSS/INS) were used to gather pseudolabels for semantic segmentation. A very simple dynamics model was used as a simulator, and dynamic parameters were obtained from the linear regression of manual driving records. Segmentation and a dynamic calibration method were found to be effective in easing the transition from a simulation to the real world. Pseudosegmentation labels are found to be more suitable for reinforcement learning models. We conducted tests on the efficacy of our proposed method, and a vehicle using the proposed system successfully drove on an unpaved track for approximately 1.8 km at an average speed of 26.57 km/h without incident. |
| format | Article |
| id | doaj-art-125eae5bfdad43e39b4389dbb64bf004 |
| institution | Kabale University |
| issn | 1687-9619 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-125eae5bfdad43e39b4389dbb64bf0042025-08-20T03:54:20ZengWileyJournal of Robotics1687-96192022-01-01202210.1155/2022/9916292Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic CalibrationJiseong Heo0Hyoung woo Lim1Agency for Defense DevelopmentAgency for Defense DevelopmentApplying reinforcement learning algorithms to autonomous driving is difficult because of mismatches between the simulation in which the algorithm was trained and the real world. To address this problem, data from global navigation satellite systems and inertial navigation systems (GNSS/INS) were used to gather pseudolabels for semantic segmentation. A very simple dynamics model was used as a simulator, and dynamic parameters were obtained from the linear regression of manual driving records. Segmentation and a dynamic calibration method were found to be effective in easing the transition from a simulation to the real world. Pseudosegmentation labels are found to be more suitable for reinforcement learning models. We conducted tests on the efficacy of our proposed method, and a vehicle using the proposed system successfully drove on an unpaved track for approximately 1.8 km at an average speed of 26.57 km/h without incident.http://dx.doi.org/10.1155/2022/9916292 |
| spellingShingle | Jiseong Heo Hyoung woo Lim Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration Journal of Robotics |
| title | Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration |
| title_full | Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration |
| title_fullStr | Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration |
| title_full_unstemmed | Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration |
| title_short | Sim-to-Real Reinforcement Learning for Autonomous Driving Using Pseudosegmentation Labeling and Dynamic Calibration |
| title_sort | sim to real reinforcement learning for autonomous driving using pseudosegmentation labeling and dynamic calibration |
| url | http://dx.doi.org/10.1155/2022/9916292 |
| work_keys_str_mv | AT jiseongheo simtorealreinforcementlearningforautonomousdrivingusingpseudosegmentationlabelinganddynamiccalibration AT hyoungwoolim simtorealreinforcementlearningforautonomousdrivingusingpseudosegmentationlabelinganddynamiccalibration |