Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing high-complexity environments. DQN simulations were conducted across four scenarios, allowing the agent to operate at speeds ranging from 60 to nearly 100 km/h. The simulations featured a variable number of vehicles/obstacles, ranging from 20 to 80, and each simulation had a duration of 40 seconds within the Highway-Env simulator. Based on the test results, the DQN method exhibited excellent performance, achieving the highest reward value in the first scenario, 35.6117 out of a maximum of 40, and a success rate of 90.075%.
Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions
2024-02-13
doi:10.62411/jcta.9929
Journal of Computing Theories and Applications; Vol 2, No 1 (2024): January-March; 54-66 ; 3024-9104
Article (Journal)
Electronic Resource
English
DDC: | 629 |
HIGHWAY TRAFFIC MODELING AND DECISION MAKING FOR AUTONOMOUS VEHICLE USING REINFORCEMENT LEARNING
British Library Conference Proceedings | 2018
|