A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control
As autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/24/9843 |
| _version_ | 1827573535281774592 |
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| author | Jihun Kim Sanghoon Park Jeesu Kim Jinwoo Yoo |
| author_facet | Jihun Kim Sanghoon Park Jeesu Kim Jinwoo Yoo |
| author_sort | Jihun Kim |
| collection | DOAJ |
| description | As autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying on autonomous driving technologies, the importance of safety features, such as fail-safe mechanisms in the event of sensor failures, has gained prominence. Therefore, this paper proposes a reinforcement learning (RL) control method for lane-keeping, which uses surrounding object information derived through LiDAR sensors instead of camera sensors for LKS. This approach uses surrounding vehicle and object information as observations for the RL framework to maintain the vehicle’s current lane. The learning environment is established by integrating simulation tools, such as IPG CarMaker, which incorporates vehicle dynamics, and MATLAB Simulink for data analysis and RL model creation. To further validate the applicability of the LiDAR sensor data in real-world settings, Gaussian noise is introduced in the virtual simulation environment to mimic sensor noise in actual operational conditions. |
| first_indexed | 2024-03-08T20:21:36Z |
| format | Article |
| id | doaj.art-0223728a01e442189836de41d78f8ae5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:21:36Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0223728a01e442189836de41d78f8ae52023-12-22T14:41:05ZengMDPI AGSensors1424-82202023-12-012324984310.3390/s23249843A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping ControlJihun Kim0Sanghoon Park1Jeesu Kim2Jinwoo Yoo3Graduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of KoreaGraduate School of Automotive Engineering, Kookmin University, Seoul 02707, Republic of KoreaDepartments of Cogno-Mechatronics Engineering and Optics and Mechatronics Engineering, Pusan National University, Busan 46241, Republic of KoreaDepartment of Automobile and IT Convergence, Kookmin University, Seoul 02707, Republic of KoreaAs autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying on autonomous driving technologies, the importance of safety features, such as fail-safe mechanisms in the event of sensor failures, has gained prominence. Therefore, this paper proposes a reinforcement learning (RL) control method for lane-keeping, which uses surrounding object information derived through LiDAR sensors instead of camera sensors for LKS. This approach uses surrounding vehicle and object information as observations for the RL framework to maintain the vehicle’s current lane. The learning environment is established by integrating simulation tools, such as IPG CarMaker, which incorporates vehicle dynamics, and MATLAB Simulink for data analysis and RL model creation. To further validate the applicability of the LiDAR sensor data in real-world settings, Gaussian noise is introduced in the virtual simulation environment to mimic sensor noise in actual operational conditions.https://www.mdpi.com/1424-8220/23/24/9843reinforcement learningautonomous vehiclesadvanced driver assistancevehicle controlsafety |
| spellingShingle | Jihun Kim Sanghoon Park Jeesu Kim Jinwoo Yoo A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control Sensors reinforcement learning autonomous vehicles advanced driver assistance vehicle control safety |
| title | A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control |
| title_full | A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control |
| title_fullStr | A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control |
| title_full_unstemmed | A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control |
| title_short | A Deep Reinforcement Learning Strategy for Surrounding Vehicles-Based Lane-Keeping Control |
| title_sort | deep reinforcement learning strategy for surrounding vehicles based lane keeping control |
| topic | reinforcement learning autonomous vehicles advanced driver assistance vehicle control safety |
| url | https://www.mdpi.com/1424-8220/23/24/9843 |
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