Abstract
In advanced driver assistance systems to conditional automation systems, monitoring of driver state is vital for predicting the driver'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-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%. 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.
| Original language | English |
|---|---|
| Article number | 8998250 |
| Pages (from-to) | 32321-32331 |
| Number of pages | 11 |
| Journal | IEEE Access |
| Volume | 8 |
| DOIs | |
| Publication status | Published - 1 Jan 2020 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- 3D motion detection
- Bistatic radar
- Doppler effect
- head movements
- STFT
- wireless sensing