21,151 research outputs found
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
Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments
Traffic waves are phenomena that emerge when the vehicular density exceeds a
critical threshold. Considering the presence of increasingly automated vehicles
in the traffic stream, a number of research activities have focused on the
influence of automated vehicles on the bulk traffic flow. In the present
article, we demonstrate experimentally that intelligent control of an
autonomous vehicle is able to dampen stop-and-go waves that can arise even in
the absence of geometric or lane changing triggers. Precisely, our experiments
on a circular track with more than 20 vehicles show that traffic waves emerge
consistently, and that they can be dampened by controlling the velocity of a
single vehicle in the flow. We compare metrics for velocity, braking events,
and fuel economy across experiments. These experimental findings suggest a
paradigm shift in traffic management: flow control will be possible via a few
mobile actuators (less than 5%) long before a majority of vehicles have
autonomous capabilities
Fuzzy logic control for energy saving in autonomous electric vehicles
Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested
A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks
One of the key ideas to make Intelligent Transportation Systems (ITS) work
effectively is to deploy advanced communication and cooperative control
technologies among the vehicles and road infrastructures. In this spirit, we
propose a consensus-based distributed speed advisory system that optimally
determines a recommended common speed for a given area in order that the group
emissions, or group battery consumptions, are minimised. Our algorithms achieve
this in a privacy-aware manner; namely, individual vehicles do not reveal
in-vehicle information to other vehicles or to infrastructure. A mobility
simulator is used to illustrate the efficacy of the algorithm, and
hardware-in-the-loop tests involving a real vehicle are given to illustrate
user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed
limits, optimised consensus, and intelligent speed advisory systems"
presented at the 3rd International Conference on Connected Vehicles and Expo
(ICCVE 2014) in November 2014. This is the revised version of the paper
recently submitted to the IEEE Transactions on Intelligent Transportation
Systems for publicatio
ITERL: A Wireless Adaptive System for Efficient Road Lighting
This work presents the development and construction of an adaptive street lighting system
that improves safety at intersections, which is the result of applying low-power Internet of Things
(IoT) techniques to intelligent transportation systems. A set of wireless sensor nodes using the
Institute of Electrical and Electronics Engineers (IEEE) 802.15.4 standard with additional internet
protocol (IP) connectivity measures both ambient conditions and vehicle transit. These measurements
are sent to a coordinator node that collects and passes them to a local controller, which then makes
decisions leading to the streetlight being turned on and its illumination level controlled. Streetlights
are autonomous, powered by photovoltaic energy, and wirelessly connected, achieving a high degree
of energy efficiency. Relevant data are also sent to the highway conservation center, allowing it to
maintain up-to-date information for the system, enabling preventive maintenance.ConsejerĂa de Fomento y Vivienda Junta de AndalucĂa G-GI3002 / IDIOFondo Europeo de Desarrollo Regional G-GI3002 / IDI
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