16,960 research outputs found

    Evaluation of Haptic Patterns on a Steering Wheel

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    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

    Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

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    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

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    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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