Name
Abstract | ||
|---|---|---|
In advanced driver assistance systems to conditional automation systems, monitoring of driver state is vital for predicting the driver & x2019;s capacity to supervise or maneuver the vehicle in cases of unexpected road events and to facilitate better in-car services. The paper presents a technique that exploits millimeter-wave Doppler radar for 3D head tracking. Identifying the bistatic and monostatic geometry for antennas to detect rotational vs. translational movements, the authors propose the biscattering angle for computing a distinctive feature set to isolate dynamic movements via class memberships. Through data reduction and joint time & x2013;frequency analysis, movement boundaries are marked for creation of a simplified, uncorrelated, and highly separable feature set. The authors report movement-prediction accuracy of 92 & x0025;. This non-invasive and simplified head tracking has the potential to enhance monitoring of driver state in autonomous vehicles and aid intelligent car assistants in guaranteeing seamless and safe journeys. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ACCESS.2020.2973957 | IEEE ACCESS |
Keywords | DocType | Volume |
Vehicles,Doppler radar,Sensors,Magnetic heads,Doppler effect,Three-dimensional displays,Bistatic radar,Doppler effect,head movements,STFT,3D motion detection,wireless sensing | Journal | 8 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muneeba Raja | 1 | 6 | 2.15 |
| Zahra Vali | 2 | 0 | 0.34 |
| Sameera Palipana | 3 | 4 | 2.50 |
| David G. Michelson | 4 | 6 | 1.22 |
| Stephan Sigg | 5 | 435 | 44.75 |