Towards Multi-Modal Driver’s Stress Detection

In this paper, we propose initial steps towards multi-modal driver stress (distraction) detection in urban driving scenarios involving multi-tasking, dialog system conversation, and medium-level cognitive tasks. The goal is to obtain a continuous operation-mode detection employing driver’s speech and CAN-Bus signals, with a direct application for an intelligent human-vehicle interface which will adapt to the actual state of the driver. First, the impact of various driving scenarios on speech production features is analyzed, followed by a design of a speech-based stress detector. In the driver-/maneuver-independent open test set task, the system reaches 88.2% accuracy in neutral/stress classification. Second, distraction detection exploiting CAN-Bus signals is introduced and evaluated in a driver-/maneuver-dependent closed test set task, reaching 98% and 84% distraction detection accuracy in lane keeping segments and curve negotiation segments, respectively. Performance of the autonomous classifiers suggests that future fusion of speech and CAN-Bus signal domains will yield an overall robust stress assessment framework.