Name
Title | ||
|---|---|---|
A Real-Time Fatigue Driving Recognition Method Incorporating Contextual Features and Two Fusion Levels. |
Abstract | ||
|---|---|---|
Though experimental results have shown a strong correlation between contextual features and the driver's fatigue state, contextual features have been applied only offline to evaluate a driver's fatigue state. This paper identifies three of the most effective contextual features, i.e., continuous driving time, sleep duration time, and current time, to facilitate the real-time (online) recognition o... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TITS.2017.2690914 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Fatigue,Real-time systems,Sleep,Support vector machines,Road safety,Computational modeling,Brain modeling | Computer vision,Support vector machine,Fusion,Correlation,Artificial intelligence,Engineering,Mathematical model,Gray relational analysis,Fuse (electrical) | Journal |
Volume | Issue | ISSN |
18 | 12 | 1524-9050 |
Citations | PageRank | References |
9 | 0.60 | 20 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| SUN Wei | 1 | 247 | 26.63 |
| Xiaorui Zhang | 2 | 144 | 17.71 |
| Srinivas Peeta | 3 | 75 | 10.17 |
| Xiaozheng He | 4 | 53 | 7.99 |
| Yongfu Li | 5 | 59 | 8.35 |