Name
Title | ||
|---|---|---|
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment. |
Abstract | ||
|---|---|---|
•A novel training platform for automated vehicle (AV)’s decision-making is proposed.•Deep deterministic policy gradient algorithm is adopted for continuous state decision of AV.•Details about reward setting and accelerated methods for DRL training are investigated.•A multi-task driving model integrating CF and LC behavior is trained simultaneously. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.trc.2019.08.011 | Transportation Research Part C: Emerging Technologies |
Keywords | Field | DocType |
Automated vehicle,Decision making,Deep reinforcement learning,Reward function | High fidelity,Computer science,Traffic model,Premise,Training program,Intelligent driver model,Artificial intelligence,Intelligent transportation system,Vehicle control,Machine learning,Reinforcement learning | Journal |
Volume | ISSN | Citations |
107 | 0968-090X | 4 |
PageRank | References | Authors |
0.43 | 17 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yingjun Ye | 1 | 4 | 0.43 |
| Xiaohui Zhang | 2 | 4 | 0.43 |
| Jian Sun | 3 | 60 | 14.76 |