GAN-Based Driver’s Head Motion Using Millimeter-Wave Radar Sensor

The recognition of driver behavior is critical for enhancing road safety, with a particular focus on monitoring driver attention. Radar-based recognition systems offer distinct advantages over traditional computer vision methods, including enhanced user privacy, reduced power consumption, and greater flexibility in sensor deployment. This study leverages a compact millimeter-wave radar to monitor driver head movements, providing a non-intrusive method to assess driver focus. A frequency-modulated continuous-wave (FMCW) radar sensor is strategically positioned on the vehicle’s steering wheel, capturing reflection patterns that vary with the driver’s head orientation. These patterns are used to identify and classify different head movements, which are indicative of the driver’s attention level. To achieve accurate classification, a deep learning approach is adopted, utilizing a Generative Adversarial Network (GAN) model. This model is particularly effective in scenarios with limited labeled data, as it can generate high-quality synthetic data to augment training. Experimental results demonstrate that the proposed method reliably classifies all relevant head movement scenarios, underscoring its potential for real-world applications in driver monitoring systems.