16,960 research outputs found
Evaluation of Haptic Patterns on a Steering Wheel
Infotainment Systems can increase mental workload and divert
visual attention away from looking ahead on the roads.
When these systems give information to the driver, provide
it through the tactile channel on the steering, it wheel might
improve driving behaviour and safety. This paper describes an
investigation into the perceivability of haptic feedback patterns
using an actuated surface on a steering wheel. Six solenoids
were embedded along the rim of the steering wheel creating
three bumps under each palm. Maximally, four of the six
solenoids were actuated simultaneously, resulting in 56 patterns
to test. Participants were asked to keep in the middle
road of the driving simulator as good as possible. Overall
recognition accuracy of the haptic patterns was 81.3%, where
identification rate increased with decreasing number of active
solenoids (up to 92.2% for a single solenoid). There was no
significant increase in lane deviation or steering angle during
haptic pattern presentation. These results suggest that drivers
can reliably distinguish between cutaneous patterns presented
on the steering wheel. Our findings can assist in delivering
non-critical messages to the driver (e.g. driving performance,
incoming text messages, etc.) without decreasing driving performance
or increasing perceived mental workload
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Driver estimation of steering wheel vibration intensity: Questionnaire-based survey
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Perception enhancement system for automotive steering
Laboratory-based experiments were conducted to
evaluate the effect of the frequency and scale of
transient vibration events on the human detection of
road surface type by means of steering wheel vibration.
The study used steering wheel tangential direction
acceleration time histories which had been measured in
a mid-sized European automobile that was driven over
three different types of road surface. The steering
acceleration stimuli were manipulated by means of the
mildly non-stationary mission synthesis (MNMS)
algorithm in order to produce test stimuli which were
selectively modified in terms of the number, and size, of
transient vibration events they contained. Fifteen test
participants were exposed to both unmanipulated and
manipulated steering wheel rotational vibration stimuli,
and were asked to indicate, by either “yes or no”,
whether the test stimuli was from a target road surface
which was displayed on a board. The findings suggested
that transient vibration events play a key role in the
human detection of road surface type in driving
situations. Improvements of up to 20 percentage points
in the rate of correct detection were achieved by means
of selective manipulation of the steering vibration
stimuli. The results also suggested, however, that no single setting of the MNMS algorithm proved optimal
for all three road surface types that were investigated
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949
Video-based driver identification using local appearance face recognition
In this paper, we present a person identification system for vehicular environments. The proposed system uses face images of the driver and utilizes local appearance-based face recognition over the video sequence. To perform local appearance-based face recognition, the input face image is decomposed into non-overlapping blocks and on each local block discrete cosine transform is applied to extract the local features. The extracted local features are then combined to construct the overall feature vector. This process is repeated for each video frame. The distribution of the feature vectors over the video are modelled using a Gaussian distribution function at the training stage. During testing, the feature vector extracted from each frame is compared to each person’s distribution, and individual likelihood scores are generated. Finally, the person is identified as the one who has maximum joint-likelihood score. To assess the performance of the developed system, extensive experiments are conducted on different identification scenarios, such as closed set identification, open set identification and verification. For the experiments a subset of the CIAIR-HCC database, an in-vehicle data corpus that is collected at the Nagoya University, Japan is used. We show that, despite varying environment and illumination conditions, that commonly exist in vehicular environments, it is possible to identify individuals robustly from their face images. Index Terms — Local appearance face recognition, vehicle environment, discrete cosine transform, fusion. 1
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