Abstract
Distracted driving has become a major cause of road traffic accidents. There are generally four different types of distractions: manual, visual, auditory, and cognitive. Manual distractions are the most common. Previous studies have used physiological indicators, vehicle behavior parameters, or machine-visual features to support research. However, these technologies are not suitable for an in-vehicle environment. To address this need, this study examined a non-intrusive method for detecting in-transit manual distractions. Wrist kinematics data from 20 drivers were collected using wearable inertial measurement units (IMU) to detect four common gestures made while driving: dialing a hand-held cellular phone, adjusting the audio or climate controls, reaching for an object in the back seat, and maneuvering the steering wheel to stay in the lane. The study proposed a progressive classification model for gesture recognition, including two major time-based sequencing components and a Hidden Markov Model (HMM). Results show that the accuracy for detecting disturbances was 95.52%. The accuracy associated with recognizing manual distractions reached 96.63%, using the proposed model. The overall model has the advantages of being sensitive to perceptions of motion, effectively solving the problem of a fall-off in recognition performance due to excessive disturbances in motion samples.
Highlights
Driver distraction and inattention is a common driving behavior that has become a growing public safety hazard
We identified a length of subsequence the trimmed sequence and served as the class template
This paper proposed a non-intrusive method for detecting in-transit manual distractions, using body kinematic measures collected from wearable inertial measurement units (IMU)
Summary
Driver distraction and inattention is a common driving behavior that has become a growing public safety hazard. Drivers have started to multitask more often, given the extensive availability of in-vehicle intelligent systems, such as smartphones and navigation devices. These tools have made driver distraction and inattention much more common in everyday driving. In most kinds of distractions, a driver lets go of or keeps only one hand on the steering wheel to complete other tasks This can lead the driver to look away from the roadway or to engage in lapses in attention and judgment. This can cause a decrease in critical information accessibility and processing capabilities required for safe driving, making the driver prone to accidents
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