Name
Abstract | ||
|---|---|---|
Recent outstanding progresses in artificial intelligence researches enable many tries to implement self-driving cars. However, in real world, there are a lot of risks and cost problems to acquire training data for self-driving artificial intelligence algorithms. This paper proposes an algorithm to collect training data from a driving game, which has quite similar environment to the real world. In the data collection scheme, the proposed algorithm gathers both driving game screen image and control key value. We employ the collected data from virtual game environment to learn a deep neural network. Experimental result for applying the virtual driving game data to drive real world children’s car show the effectiveness of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICAIIC.2019.8669037 | 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) |
Keywords | Field | DocType |
Games,Artificial intelligence,Training data,Autonomous automobiles,Data collection,Neural networks,Automobiles | Training set,Data collection,Virtual machine,Computer science,Algorithm,Virtual game,Artificial intelligence,Deep learning,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-5386-7822-0 | 1 | 0.37 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juntae Kim | 1 | 9 | 8.72 |
| GeunYoung Lim | 2 | 1 | 0.37 |
| Youngi Kim | 3 | 1 | 0.37 |
| Bokyeong Kim | 4 | 1 | 0.37 |
| Changseok Bae | 5 | 161 | 23.90 |