Name
Title | ||
|---|---|---|
Dynamic speed trajectory generation and tracking control for autonomous driving of intelligent high-speed trains combining with deep learning and backstepping control methods |
Abstract | ||
|---|---|---|
The development of autonomous transportation systems has received increasing attention over the last decades. Different from existing automatic train control systems, the decision-making capability in the autonomous driving system enables a train to adapt to the complicated and dynamic circumstances. This paper in particular focuses on the decision-making problem for the autonomous driving of intelligent high-speed trains, and proposes a novel decision-making framework by combining the deep learning and backstepping control methods. By exploiting the deep learning methods, a speed trajectory generator is trained with the actual driving data, and dynamically calculates the reference speed trajectory according to the real-time driving condition. Then, a backstepping controller is designed to track the reference speed trajectory such that the separation, cohesion and alignment requirements for the autonomous driving of high-speed trains are achieved. Simulation experiments are implemented to illustrate the effectiveness of our methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.engappai.2022.105230 | Engineering Applications of Artificial Intelligence |
Keywords | DocType | Volume |
Autonomous driving,High-speed trains,Tracking control,Deep learning,Backstepping control | Journal | 115 |
ISSN | Citations | PageRank |
0952-1976 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Wang | 1 | 11 | 4.53 |
| Shukai Li | 2 | 108 | 11.61 |
| Yuan Cao | 3 | 73 | 10.39 |
| Tianpeng Xin | 4 | 0 | 0.34 |
| Lixing Yang | 5 | 274 | 28.39 |