Decision-making of autonomous driving based on reinforcement learning
Autonomous driving (AD) technology has garnered significant interest in recent years due to its potential to transform transportation. However, despite advancements in AD technologies, current vehicles on the road are only partially autonomous, with limited autonomous features. Among the different s...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167670 |
| _version_ | 1811676260584128512 |
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| author | Lee, Julia Hui Hui |
| author2 | Lyu Chen |
| author_facet | Lyu Chen Lee, Julia Hui Hui |
| author_sort | Lee, Julia Hui Hui |
| collection | NTU |
| description | Autonomous driving (AD) technology has garnered significant interest in recent years due to its potential to transform transportation. However, despite advancements in AD technologies, current vehicles on the road are only partially autonomous, with limited autonomous features. Among the different stages in the AD pipeline, the decision-making process, particularly the prediction stage, has received relatively less attention and development compared to other modules. This is concerning as the decision-making stage is crucial for the safe and efficient operation of autonomous vehicles in any environment. Although there are existing studies on End-to-End Autonomous Driving, it does not provide enough insights into the selection and evaluation of reinforcement learning (RL) models for decision-making in autonomous driving tasks. Therefore, this paper is intended to investigate and compare the performance of two commonly used RL models, Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), in a simulated autonomous driving scenario. The models are evaluated based on quantitative performance metrics such as collision rate, goal reached rate, and average distance covered, as well as qualitative behaviors observed during simulation runs. |
| first_indexed | 2024-10-01T02:18:39Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167670 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:18:39Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1676702023-06-03T16:50:29Z Decision-making of autonomous driving based on reinforcement learning Lee, Julia Hui Hui Lyu Chen School of Mechanical and Aerospace Engineering lyuchen@ntu.edu.sg Engineering::Mechanical engineering::Motor vehicles Autonomous driving (AD) technology has garnered significant interest in recent years due to its potential to transform transportation. However, despite advancements in AD technologies, current vehicles on the road are only partially autonomous, with limited autonomous features. Among the different stages in the AD pipeline, the decision-making process, particularly the prediction stage, has received relatively less attention and development compared to other modules. This is concerning as the decision-making stage is crucial for the safe and efficient operation of autonomous vehicles in any environment. Although there are existing studies on End-to-End Autonomous Driving, it does not provide enough insights into the selection and evaluation of reinforcement learning (RL) models for decision-making in autonomous driving tasks. Therefore, this paper is intended to investigate and compare the performance of two commonly used RL models, Proximal Policy Optimization (PPO) and Deep Q-Network (DQN), in a simulated autonomous driving scenario. The models are evaluated based on quantitative performance metrics such as collision rate, goal reached rate, and average distance covered, as well as qualitative behaviors observed during simulation runs. Bachelor of Engineering (Mechanical Engineering) 2023-05-30T06:35:03Z 2023-05-30T06:35:03Z 2023 Final Year Project (FYP) Lee, J. H. H. (2023). Decision-making of autonomous driving based on reinforcement learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167670 https://hdl.handle.net/10356/167670 en C080 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Mechanical engineering::Motor vehicles Lee, Julia Hui Hui Decision-making of autonomous driving based on reinforcement learning |
| title | Decision-making of autonomous driving based on reinforcement learning |
| title_full | Decision-making of autonomous driving based on reinforcement learning |
| title_fullStr | Decision-making of autonomous driving based on reinforcement learning |
| title_full_unstemmed | Decision-making of autonomous driving based on reinforcement learning |
| title_short | Decision-making of autonomous driving based on reinforcement learning |
| title_sort | decision making of autonomous driving based on reinforcement learning |
| topic | Engineering::Mechanical engineering::Motor vehicles |
| url | https://hdl.handle.net/10356/167670 |
| work_keys_str_mv | AT leejuliahuihui decisionmakingofautonomousdrivingbasedonreinforcementlearning |