The appearance of cars has raised materialistic civilization and living standard to an unprecedented level. Today, it is hard to imagine how we human beings can live without cars. Yet, motor vehicles can cause a great number of deaths and injuries as well as considerable economic losses, which have constituted the global burden. Understanding of the occurrence and development of road traffic injuries will contribute to the prevention and control of crash and to the implementation of "everybody has the right to enjoy health" proposed by WHO.

Road traffic injuries.

Blockchain technology, while still challenged with key limitations, is a transformative Information and Communications Technology (ICT) that has changed our notion of trust. Improved efficiencies for agricultural sustainable development has been demonstrated when ICT-enabled farms have access to knowledge banks and other digital resources. UN FAO-recommended ICT e-agricultural infrastructure components are a confluence of ICT and blockchain technology requirements. When ICT e-agricultural systems with blockchain infrastructure are immutable and distributed ledger systems for record management, baseline agricultural environmental data integrity is safeguarded for those who participate in transparent data management. This paper reviewed blockchain-based concepts associated with ICT-based technology. Moreover, a model ICT e-agriculture system with a blockchain infrastructure is proposed for use at the local and regional scale. To determine context specific technical and social requirements of blockchain technology for ICT e-agriculture systems, an evaluation tool is presented. The proposed system and tool can be evaluated and applied to further developments of e-agriculture systems.

https://www.mdpi.com/2076-3298/4/3/50/pdf

Blockchain: The Evolutionary Next Step for ICT E-Agriculture

Driver inattention and distraction are the main causes of road accidents, many of which result in fatalities. To reduce road accidents, the development of information systems to detect driver inattention and distraction is essential. Currently, distraction detection systems for road vehicles are not yet widely available or are limited to specific causes of driver inattention such as driver fatigue. Despite the increasing automation of driving due to the availability of increasingly sophisticated assistance systems, the human driver will continue to play a longer role as supervisor of vehicle automation. With this in mind, we review the published scientific literature on driver distraction detection methods and integrate the identified approaches into a holistic framework that is the main contribution of the paper. Based on published scientific work, our driver distraction detection framework contains a structured summary of reviewed approaches for detecting the three main distraction detection approaches: manual distraction, visual distraction, and cognitive distraction. Our framework visualizes the whole detection information chain from used sensors, measured data, computed data, computed events, inferred behavior, and inferred distraction type. Besides providing a sound summary for researchers interested in distracted driving, we discuss several practical implications for the development of driver distraction detection systems that can also combine different approaches for higher detection quality. We think our research can be useful despite - or even because of - the great developments in automated driving.

/pdf/driver-distraction-detection-methods-a-literature-review-and-2emcu11f8k.pdf

Driver Distraction Detection Methods: A Literature Review and Framework

Blockchain for precision irrigation: Opportunities and challenges

With smartphones being so ubiquitous, more connected and largely fitted with several types of sensors such as GPS, microphones, cameras, magnetometers, accelerometers, etc; there is an increasing opportunity in the development of smartphone-based sensor systems. In this article, we propose a short survey on the use of smartphone sensors in the detection of various kinds of anomalies in several fields namely environment, agriculture, healthcare and road/traffic conditions. We also list the main advantages and limitations of the use of smartphone sensors systems in such fields.

Anomalies Detection Through Smartphone Sensors: A Review

Road safety is a major public health issue the world over. Many studies have found that the primary factors responsible for road accidents center on the driver and her/his driving. Hence, there is the need to monitor driver's state and her/his driving, with a view to providing effective feedback. Our proposed demo is of HAMS, a windshield-mounted, smartphone-based system that uses the front camera to monitor the driver and back camera to monitor her/his driving behaviour. The objective of HAMS is to provide ADAS-like functionality with low-cost devices that can be retrofitted onto the large installed base of vehicles that lack specialized and expensive sensors such as LIDAR and RADAR. Our demo would show HAMS in action on an Android smartphone to monitor the state of the driver, specifically such as drowsiness, distraction and gaze, and vehicle ranging, lane detection running on pre-recorded videos from drives.

HAMS: Driver and Driving Monitoring using a Smartphone

Roads form a critical part of any region's infrastructure. Their constant monitoring and maintenance is thus essential. Traditional monitoring mechanisms are heavy-weight, and hence have insufficient coverage. In this paper, we explore the use of crowd-sourced intelligent measurements from commuters' smart-phone sensors. Specifically, we propose a deep-learning based approach to road surface quality monitoring, using accelerometer and GPS sensor readings. Through extensive data collection of over 36 hours on different kinds of roads, and subsequent evaluation based on this, we show that the approach can achieve high accuracy (98.5%) in a three-way classification of road surface quality. We also show how the classification can be extended to a finer grained 11-point scale of road quality. The model is also efficient: it can be implemented on today's smart-phones, thus making it practical. Our approach, called RoadCare, enables several useful smart-city applications such as spatio-temporal monitoring of the city's roads, early warning of bad road conditions, as well as choosing the "smoothest" road route to a destination.

RoadCare: A Deep-learning Based Approach to Quantifying Road Surface Quality

Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle’s lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, or whether the vehicle’s speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this article, we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle’s current lane. We employ a deep learning--based technique to classify the vehicle’s lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions. We perform extensive evaluation of DeepLane on real-world datasets collected in developed and developing regions. DeepLane can detect a vehicle’s lane position with an accuracy of over 90%, and we have implemented DeepLane as an Android app.

Driving Lane Detection on Smartphones using Deep Neural Networks

Current smartphone-based navigation applications fail to provide lane-level information due to poor GPS accuracy. Detecting and tracking a vehicle's lane position on the road assists in lane-level navigation. For instance, it would be important to know whether a vehicle is in the correct lane for safely making a turn, perhaps even alerting the driver in advance if it is not, or whether the vehicle's speed is compliant with a lane-specific speed limit. Recent efforts have used road network information and inertial sensors to estimate lane position. While inertial sensors can detect lane shifts over short windows, it would suffer from error accumulation over time. In this paper we present DeepLane, a system that leverages the back camera of a windshield-mounted smartphone to provide an accurate estimate of the vehicle's current lane. We employ a deep learning based technique to classify the vehicle's lane position. DeepLane does not depend on any infrastructure support such as lane markings and works even when there are no lane markings, a characteristic of many roads in developing regions.We perform extensive evaluation of DeepLane on real world datasets collected in developed and developing regions. DeepLane can detect vehicle's lane position with an accuracy of over 90% in both day and night conditions. We have implemented DeepLane as an Android-app that runs at 5 fps on CPU and upto 15 fps on smart-phone's GPU and can also assist existing navigation applications with lane-level information.

DeepLane: camera-assisted GPS for driving lane detection

Today, Information and Communication Technology (ICT) is ubiquitous. It has touched almost all spheres concerning human survival, growth and development. Agriculture is no exception to it. This paper presents the research initiatives towards improving irrigation efficiency through affordable ICT solutions. It presents experimental results of studies related to improving irrigation efficiency in Semi arid zones in India, using commercially available ICT solutions. It compares between simple microirrigation and microirrigation with ICT (irrigation on demand) and establishes that by using ICT in agriculture, we can increase the irrigation efficiencies by a considerable amount. The paper also presents the development of indigenous wireless sensor node and gateway made from open source hardware and software components. The efforts in this regard show, while maintaining comparable performance, a significant reduction in the cost of wireless sensor devices, when compared to commercially available wireless sensor devices, is achieved.

Ravi Bhandari

Papers

HAMS: Driver and Driving Monitoring using a Smartphone

RoadCare: A Deep-learning Based Approach to Quantifying Road Surface Quality

Driving Lane Detection on Smartphones using Deep Neural Networks

DeepLane: camera-assisted GPS for driving lane detection

Development of affordable ICT solutions for water conservation in agriculture