Name
Abstract | ||
|---|---|---|
The ability of modeling and predicting vehicle motion activities is important for automated vehicles. In this paper, we propose an And-Or Graph based model to give a simple and clear description of motion activities. Compared to other models, this new model relaxes the Markov property requirement in transition between activities and is thus more flexible. The parameters of this model can be easily learned from data. Using the trained new model, we can predict the on-going motion activity label and its corresponding probability. Experiments show that a high prediction accuracy (97%) can be achieved by this new model. |
Year | Venue | Field |
|---|---|---|
2018 | Intelligent Vehicles Symposium | Data modeling,Graph,Markov property,Task analysis,Computer science,Algorithm,Prediction algorithms,Acceleration |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wang Shuofeng | 1 | 4 | 2.13 |
| Li Li | 2 | 581 | 109.68 |
| Nanning Zheng | 3 | 3975 | 329.18 |
| Dongpu Cao | 4 | 75 | 3.80 |