Lane Change Decision-Making through Deep Reinforcement Learning
Due to the complexity and volatility of the traffic environment, decision-making in autonomous driving is a significantly hard problem. In this project, we use a Deep Q-Network, along with rule-based constraints to make lane-changing decision. A safe and efficient lane change behavior may be obtaine...
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| creator | Ghimire, Mukesh Choudhury, Malobika Roy Lagudu, Guna Sekhar Sai Harsha |
| description | Due to the complexity and volatility of the traffic environment,
decision-making in autonomous driving is a significantly hard problem. In this
project, we use a Deep Q-Network, along with rule-based constraints to make
lane-changing decision. A safe and efficient lane change behavior may be
obtained by combining high-level lateral decision-making with low-level
rule-based trajectory monitoring. The agent is anticipated to perform
appropriate lane-change maneuvers in a real-world-like udacity simulator after
training it for a total of 100 episodes. The results shows that the rule-based
DQN performs better than the DQN method. The rule-based DQN achieves a safety
rate of 0.8 and average speed of 47 MPH |
| doi_str_mv | 10.48550/arxiv.2112.14705 |
| format | Article |
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decision-making in autonomous driving is a significantly hard problem. In this
project, we use a Deep Q-Network, along with rule-based constraints to make
lane-changing decision. A safe and efficient lane change behavior may be
obtained by combining high-level lateral decision-making with low-level
rule-based trajectory monitoring. The agent is anticipated to perform
appropriate lane-change maneuvers in a real-world-like udacity simulator after
training it for a total of 100 episodes. The results shows that the rule-based
DQN performs better than the DQN method. The rule-based DQN achieves a safety
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decision-making in autonomous driving is a significantly hard problem. In this
project, we use a Deep Q-Network, along with rule-based constraints to make
lane-changing decision. A safe and efficient lane change behavior may be
obtained by combining high-level lateral decision-making with low-level
rule-based trajectory monitoring. The agent is anticipated to perform
appropriate lane-change maneuvers in a real-world-like udacity simulator after
training it for a total of 100 episodes. The results shows that the rule-based
DQN performs better than the DQN method. The rule-based DQN achieves a safety
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decision-making in autonomous driving is a significantly hard problem. In this
project, we use a Deep Q-Network, along with rule-based constraints to make
lane-changing decision. A safe and efficient lane change behavior may be
obtained by combining high-level lateral decision-making with low-level
rule-based trajectory monitoring. The agent is anticipated to perform
appropriate lane-change maneuvers in a real-world-like udacity simulator after
training it for a total of 100 episodes. The results shows that the rule-based
DQN performs better than the DQN method. The rule-based DQN achieves a safety
rate of 0.8 and average speed of 47 MPH</abstract><doi>10.48550/arxiv.2112.14705</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Learning Computer Science - Robotics |
| title | Lane Change Decision-Making through Deep Reinforcement Learning |
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