Name
Abstract | ||
|---|---|---|
This paper presents an evolving cloud-based algorithm for detecting driver actions and is actually a continuation of our earlier work. The general idea is to develop a system that is able to detect different maneuvers that drivers perform while driving a car. With that we want to detect not only the type but also the time window when the maneuver was performed. As this paper shows, maneuver detection could be done by analyzing the basic senors and signals normally measured in a car, such as revolutions, speed, steering wheel angle, pedal position and others. Moreover, the proposed method does not require any additional (intelligent) sensors such as cameras, radar, etc. On the basis of these signals we can identify two new maneuvers: U-turn and 3-point turn. For the experiment purposes we have acquired a real data from a car simulator with experienced drivers. The experiments show that the evolving fuzzy cloud-based system efficiently deals with detecting of driver maneuvers. Easily we could adapt the algorithm for online processing, analyzing and detecting the maneuvers in real time. Therefore, the proposed method is suitable for real applications. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/SSCI47803.2020.9308520 | 2020 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | DocType | ISBN |
driver maneuvers,driver actions,car simulator,maneuver detection,fuzzy cloud-based system | Conference | 978-1-7281-2548-0 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Goran Andonovski | 1 | 0 | 1.01 |
| Oscar Sipele | 2 | 0 | 0.34 |
| José Antonio Iglesias | 3 | 261 | 20.54 |
| Araceli Sanchis | 4 | 357 | 40.26 |
| Edwin Lughofer | 5 | 1940 | 99.72 |
| Igor Skrjanc | 6 | 354 | 52.47 |