Browsing by Subject "driver information systems"

Now showing 1 - 3 of 3
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    Deployment of SE-SqueezeNext on NXP BlueBox 2.0 and NXP i.MX RT1060 MCU
    (IEEE, 2020-08) Chappa, Ravi Teja N. V. S.; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    Convolution neural system is being utilized in field of self-governing driving vehicles or driver assistance systems (ADAS), and has made extraordinary progress. Before the CNN, conventional AI calculations helped ADAS. Right now, there is an incredible investigation being done in DNNs like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN designs and made it increasingly appropriate to actualize on real-time embedded systems. Due to the model size complexity of many models, they cannot be deployed straight away on real-time systems. The most important requirement will be to have less model size without a tradeoff with accuracy. Squeeze-and-Excitation SqueezeNext which is an efficient DNN with best model accuracy of 92.60% and with least model size of 0.595MB is chosen to be deployed on NXP BlueBox 2.0 and NXP i.MX RT1060. This deployment is very successful because of its less size and better accuracy. The model is trained and validated on CIFAR-10 dataset.
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    Direct Vehicle Collision Detection from Motion in Driving Video
    (IEEE, 2017-06) Kilicarslan, Mehmet; Zheng, Jiang Yu; Computer and Information Science, School of Science
    The objective of this work is the instantaneous computation of Time-to-Collision (TTC) for potential collision only from motion information captured with a vehicle borne camera. The contribution is the detection of dangerous events and degree directly from motion divergence in the driving video, which is also a clue used by human drivers, without applying vehicle recognition and depth measuring in prior. Both horizontal and vertical motion divergence are analyzed simultaneously in several collision sensitive zones. Stable motion traces of linear feature components are obtained through filtering in the motion profiles. As a result, this avoids object recognition, and sophisticated depth sensing. The fine velocity computation yields reasonable TTC accuracy so that the video camera can achieve collision avoidance alone from size changes of visual patterns.
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    Sparse Coding of Weather and Illuminations for ADAS and Autonomous Driving
    (IEEE, 2018-06) Cheng, Guo; Zheng, Jiang Yu; Murase, Hiroshi; Computer and Information Science, School of Science
    Weather and illumination are critical factors in vision tasks such as road detection, vehicle recognition, and active lighting for autonomous vehicles and ADAS. Understanding the weather and illumination type in a vehicle driving view can guide visual sensing, control vehicle headlight and speed, etc. This paper uses sparse coding technique to identify weather types in driving video, given a set of bases from video samples covering a full spectrum of weather and illumination conditions. We sample traffic and architecture insensitive regions in each video frame for features and obtain clusters of weather and illuminations via unsupervised learning. Then, a set of keys are selected carefully according to the visual appearance of road and sky. For video input, sparse coding of each frame is calculated for representing the vehicle view robustly under a specific illumination. The linear combination of the basis from keys results in weather types for road recognition, active lighting, intelligent vehicle control, etc.