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Game Theoretic Analysis of Road User Safety Scenarios Involving Autonomous Vehicles
Interactions between pedestrians, bikers, and human-driven vehicles have...
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Autonomous Cars: Vision based Steering Wheel Angle Estimation
Machine learning models, which are frequently used in self-driving cars,...
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Detecting Unsigned Physical Road Incidents from Driver-View Images
Safety on roads is of uttermost importance, especially in the context of...
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Road Segmentation Using CNN with GRU
This paper presents an accurate and fast algorithm for road segmentation...
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Road Quality Analysis Based on Cognitive Internet of Vehicles (CIoV)
This research proposal aims to use cognitive methods to analyze the qual...
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An Improved Deep Convolutional Neural Network-Based Autonomous Road Inspection Scheme Using Unmanned Aerial Vehicles
Advancements in artificial intelligence (AI) gives a great opportunity t...
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A Novel Deep Neural Network Architecture for Mars Visual Navigation
In this paper, emerging deep learning techniques are leveraged to deal w...
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JuncNet: A Deep Neural Network for Road Junction Disambiguation for Autonomous Vehicles
With a great amount of research going on in the field of autonomous vehicles or self-driving cars, there has been considerable progress in road detection and tracking algorithms. Most of these algorithms use GPS to handle road junctions and its subsequent decisions. However, there are places in the urban environment where it becomes difficult to get GPS fixes which render the junction decision handling erroneous or possibly risky. Vision-based junction detection, however, does not have such problems. This paper proposes a novel deep convolutional neural network architecture for disambiguation of junctions from roads with a high degree of accuracy. This network is benchmarked against other well known classifying network architectures like AlexNet and VGGnet. Further, we discuss a potential road navigation methodology which uses the proposed network model. We conclude by performing an experimental validation of the trained network and the navigational method on the roads of the Indian Institute of Science (IISc).
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