Gaze dynamics with spatiotemporal guided feature descriptor for prediction of driver’s maneuver behavior

At different levels of driving automation, driver’s gaze maintains great indispensable importance on semantic perception of the surround. In this work, we model gaze dynamics and clarify its relationship with driver’s maneuver behaviors from personalized driving style. Firstly, this paper proposes an Occlusion-immune Face Detector (OFD) for facial landmark detection, which can adaptively solve the facial occlusion introduced by the body and glasses frame in the real-world driving scenarios. Meanwhile, an Eye-head Coordination Model is brought up to bridge the error gap in gaze direction through determining eye pose and head pose fused pattern. Then, a vectorized spatiotemporal guidance feature (STGF) descriptor combining gaze accumulation and gaze transition frequency is proposed to construct gaze dynamics within a time window. Finally, we predict driver’s maneuver behavior through STGF descriptor considering different driving styles to clarify the relationship between gaze dynamics and driving maneuver task. Natural driving data are sampled in a dedicated instrumented vehicle testbed, on which 15 drivers with three kind of driving styles participated. Experimental results show that the prediction model achieves the best performance, estimating driver’s behavior an average of 1 s ahead of actual behavior with 83.6% accuracy considering driving style, compared with other approaches.