4:50 PM - 5:10 PM
[3Z2-04] Efficiency of Traffic Flow with Mutual Concessions of Autonomous Cars Using Deep Q-Network
Keywords:Deep Q-Network, Multi agent, autonomous driving
In recent years, the development of autonomous operation technology has been actively carried out in a various research institutions and companies. Many experiments are conducted in public road to confirm whether one autonomous car can drive safely. However, autonomous operation with inter-vehicle communication has not been much researched.
In this paper, we implement mutual concessions of autonomous cars with Deep Q-Network (DQN), which is a deep neural network structure used for estimation of Q-value of the Q-learning method.
To verify the effectiveness of mutual concessions, we develop an experiment environment for verification of autonomous operation with radio control (RC) cars.
We implement mutual concessions of autonomous cars at the confluence in the roundabout. DQN is applied for the decision-making mechanism to decide velocity in the roundabout based on the position of others and the status of congestion. As a result of our experiment, we confirmed that the autonomous cars with DQN can realize high transfer efficiency in the roundabout.
In this paper, we implement mutual concessions of autonomous cars with Deep Q-Network (DQN), which is a deep neural network structure used for estimation of Q-value of the Q-learning method.
To verify the effectiveness of mutual concessions, we develop an experiment environment for verification of autonomous operation with radio control (RC) cars.
We implement mutual concessions of autonomous cars at the confluence in the roundabout. DQN is applied for the decision-making mechanism to decide velocity in the roundabout based on the position of others and the status of congestion. As a result of our experiment, we confirmed that the autonomous cars with DQN can realize high transfer efficiency in the roundabout.