Name
Title | ||
|---|---|---|
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning. |
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Robotics | Journal |
Volume | Citations | PageRank |
abs/1904.10171 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyu Shi | 1 | 0 | 1.35 |
| Pin Wang | 2 | 1 | 2.17 |
| Xuxin Cheng | 3 | 0 | 0.34 |
| Ching-Yao Chan | 4 | 79 | 23.48 |