Published on Tue Apr 23 2019

Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning

Tianyu Shi, Pin Wang, Xuxin Cheng, Ching-Yao Chan, Ding Huang

We apply Deep Q-network (DQN) with the consideration of safety during the maneuver. Pure Pursuit Control is implemented for path tracking. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.

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Abstract

We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial lane change trajectory is generated and Pure Pursuit Control is implemented for path tracking. We demonstrate the effectiveness of this framework in simulation, from both the decision-making and control layers. The proposed architecture also has the potential to be extended to other autonomous driving scenarios.

Sat Mar 30 2019
Artificial Intelligence
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A Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task. With the setting of our state representation and reward function, the trained agent is able to take appropriate actions in a real-world-like simulator.
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Artificial Intelligence
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Artificial Intelligence
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Artificial Intelligence
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