Towards Multimodal Driver’s Stress Detection - Digital Signal Processing for In-Vehicle Systems and Safety - page 2

Chapter 1
Towards Multimodal Driver's Stress Detection
Hynek Bo ˇ il, Pinar Boyraz, and John H.L. Hansen
Abstract Non-driving-related cognitive load and variations of emotional state
may impact the drivers' capability to control a vehicle and introduce driving errors.
The availability of stress detection in drivers would benefit the design of active
safety systems and other intelligent in-vehicle interfaces. In this chapter, we
propose initial steps towards multimodal driver stress (distraction) detection in
urban driving scenarios involving multitasking, 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.
Keywords Active safety ￿ CAN-bus signal processing ￿ Distraction detection
￿ Stress
H. Boˇil ( * ) ￿ P. Boyraz ￿ J.H.L. Hansen
Center for Robust Speech Systems, Erik Jonsson School of Engineering
& Computer Science, University of Texas at Dallas, Richardson, TX, USA
e-mail: [e-mail] ; [e-mail] ; [e-mail]