Name
Abstract | ||
|---|---|---|
•A car-following model was proposed based on deep reinforcement learning.•It uses speed deviations as reward function and considers a reaction delay of 1 s.•Deep deterministic policy gradient algorithm was used to optimize the model.•The model outperformed traditional and recent data-driven car-following models.•The model demonstrated good capability of generalization. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.trc.2018.10.024 | Transportation Research Part C: Emerging Technologies |
Keywords | DocType | Volume |
Autonomous car following,Human-like driving planning,Deep reinforcement learning,Naturalistic driving study,Deep deterministic policy gradient | Journal | 97 |
ISSN | Citations | PageRank |
0968-090X | 6 | 0.55 |
References | Authors | |
16 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meixin Zhu | 1 | 14 | 1.23 |
| Xuesong Wang | 2 | 27 | 5.86 |
| Yinhai Wang | 3 | 292 | 39.37 |