scholarly journals Machine Learning to Classify Driving Events Using Mobile Phone Sensors Data

2021 ◽  
Vol 15 (02) ◽  
pp. 124
Author(s):  
Yazan Alqudah ◽  
Belal Sababha ◽  
Esam Qaralleh ◽  
Tarek Yousseff
Keyword(s):  
Machine Learning ◽  
Road Safety ◽  
Autonomous Vehicles ◽  
Machine Learning Algorithms ◽  
Motion Sensors ◽  
The Road ◽  
Crowd Sensing ◽  
Innovative Solution ◽  
On The Road ◽  
Self Driving Cars

With the ever-increasing vehicle population and introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces an innovative solution that aims at understanding vehicle behavior based on sensors data. The behavior is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving and consuming a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption.  This measure will become more valuable as more autonomous vehicles and more ride sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce energy consumption.  By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors and are used to train machine learning algorithms to classify the events.

scholarly journals IoT Bus Navigation System with Optimized Routing using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 1-15
Author(s):  
Samer I. Mohamed ◽  
◽  
Muhamed Abdelhadi
Keyword(s):  
Machine Learning ◽  
Public Transportation ◽  
Operating Cost ◽  
Machine Learning Algorithms ◽  
Layered System ◽  
The Road ◽  
Chip Technology ◽  
On The Road ◽  
On Chip ◽  
Maintenance Schedules

As the population in Egypt is ever expanding, it is reflected in the increase of the number of vehicles on the road. Public transportation is the solution and the number of available buses can cover a significant amount of the population demand. However, the outdated state of the transportation infrastructure, the static nature of the lines and indistinct schedules create a confounding and unappealing user experience which prompts the users to stray to cars for their needs. So, an Intelligent Urban Transportation System (IUTS) is a must. IUTS is a multi-layered system which provides the solution for most of these problems. It operates on different layers starting from a real time vehicle tracking for transparent and efficient management of assets, cash-less ticketing done through RFID cards, vehicle health and diagnostic data for creation of automated maintenance schedules and a friendly interactive driver interface. In this paper an approach based on combining all these technologies is discussed where the hardware component is implemented based on System-on-Chip technology with custom hardware to interface with the vehicle. The data collected from the on-board unit is sent to the cloud, and with the help of machine learning algorithms the dynamic responsiveness of the system is guaranteed. The proposed system outperforms other existing ones through the dynamic and optimized routing feature for the bus navigation to optimize the operating cost but still satisfy the passengers’demand.

scholarly journals A PRELIMINARY STUDY ON UPDATING HIGH DEFINITION MAPS: DETECTING AND POSITIONING A TRAFFIC CONE BY USING A STEREO CAMERA

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 271-274
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Real Time ◽  
Data Storage ◽  
Autonomous Vehicles ◽  
Detection System ◽  
Time Data ◽  
High Definition ◽  
Stereo Camera ◽  
The Road ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous Vehicles (AV) or self-driving cars has been increasingly popular these past few years. As such, research and development of AVs have also escalated around the world. One of those researches is about High-Definition (HD) maps. HD Maps are basically very detailed maps that provide all the geometric and semantic information on the road, which helps the AV in positioning itself on the lanes as well as mapping objects and markings on the road. This research will focus on the early stages of updating said HD maps. The methodology mainly consists of (1) running YOLOv3, a real-time object detection system, on a photo taken from a stereo camera to detect the object of interest, in this case a traffic cone, (2) applying the theories of stereo-photogrammetry to determine the 3D coordinates of the traffic cone, and (3) executing all of it at the same time on a Python-based platform. Results have shown centimeter-level accuracy in terms of obtained distance and height of the detected traffic cone from the camera setup. In future works, observed coordinates can be uploaded to a database and then connected to an application for real-time data storage/management and interactive visualization.

Regaining Sight of Humanity on The Roadway towards Automation

2020 ◽  
Vol 2020 (16) ◽  
pp. 88-1-88-5
Author(s):  
Mónica López-González
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Machine Learning Algorithms ◽  
Technological Advancement ◽  
Cognitive Intelligence ◽  
Safety Standards ◽  
The Road ◽  
Technological Advances ◽  
On The Road ◽  
Problem Solve

A primary goal of the auto industry is to revolutionize transportation with autonomous vehicles. Given the mammoth nature of such a target, success depends on a clearly defined balance between technological advances, machine learning algorithms, physical and network infrastructure, safety, standards and regulations, and end-user education. Unfortunately, technological advancement is outpacing the regulatory space and competition is driving deployment. Moreover, hope is being built around algorithms that are far from reaching human-like capacities on the road. Since human behaviors and idiosyncrasies and natural phenomena are not going anywhere anytime soon and so-called edge cases are the roadway norm, the industry stands at a historic crossroads. Why? Because human factors such as cognitive and behavioral insights into how we think, feel, act, plan, make decisions, and problem-solve have been ignored. Human cognitive intelligence is foundational to driving the industry’s ambition forward. In this paper I discuss the role of the human in bridging the gaps between autonomous vehicle technology, design, implementation, and beyond.

scholarly journals Autonomous Drifting Using Reinforcement Learning

2021 ◽  
Author(s):  
László Orgován ◽  
Tamás Bécsi ◽  
Szilárd Aradi
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Model Free ◽  
On The Road ◽  
Self Driving Cars ◽  
Planning Problems

Autonomous vehicles or self-driving cars are prevalent nowadays, many vehicle manufacturers, and other tech companies are trying to develop autonomous vehicles. One major goal of the self-driving algorithms is to perform manoeuvres safely, even when some anomaly arises. To solve these kinds of complex issues, Artificial Intelligence and Machine Learning methods are used. One of these motion planning problems is when the tires lose their grip on the road, an autonomous vehicle should handle this situation. Thus the paper provides an Autonomous Drifting algorithm using Reinforcement Learning. The algorithm is based on a model-free learning algorithm, Twin Delayed Deep Deterministic Policy Gradients (TD3). The model is trained on six different tracks in a simulator, which is developed specifically for autonomous driving systems; namely CARLA.

scholarly journals Human-Computer Interaction with Special Emphasis on Converting Brain Signals to Speech

2020 ◽  
Vol 9 (6) ◽  
pp. 523-529
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Science Fiction ◽  
Linear Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Road ◽  
Brain Signals ◽  
On The Road ◽  
The Brain

Huge hurdle neuro engineers face on the road to effective brain-computer interfaces is attempting to translate the big selection of signals made by our brain into words pictures which may be simply communicable. The science-fiction plan of having the ability to manage devices or communicate with others simply by thinking is slowly but surely, obtaining nearer to reality. Translating brainwaves into words has been another large challenge for researchers, but again with the help of machine learning algorithms, superb advances are seen in recent years. The exploitation of deep learning and acceptable machine learning algorithms, the management signals from the brain will regenerate to some actions or some speech or text. For this, a neural network is created for the brain and conjointly a mapping is completed to catch all the brain signals in which neural network will be additionally used for changing these signals into actions. From the past literature, it is being concluded that the Deep Neural Networks are one of the main algorithms that are being placed into use for this research. This review article majorly focuses on studying the behavioral patterns generated by the brain signals and how they can be converted into actions effectively so that people suffering from semi or full paralysis can use this technology to live a normal life if not completely but to a certain extent. Also, it focuses on analyzing and drawing a comparison between linear and non-linear models and to conclude the best-suited model for the same currently available to the researchers.

scholarly journals USING DEEP LEARNING TO DIGITIZE ROAD ARROW MARKINGS FROM LIDAR POINT CLOUD DERIVED IMAGES

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 123-129
Author(s):  
M. L. R. Lagahit ◽  
Y. H. Tseng
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Point Cloud ◽  
Semantic Information ◽  
High Definition ◽  
The Road ◽  
Learning Techniques ◽  
Local Coordinates ◽  
On The Road ◽  
Self Driving Cars

Abstract. The concept of Autonomous vehicles or self-driving cars has recently been gaining a lot of popularity. Because of this, a lot of research is being done to develop the technology. One of which is High Definition (HD) Maps, which are centimeter-level precision 3D maps that contain a lot of geometric and semantic information about the road which can assist the AV when driving. An important component of HD maps is the road markings which indicates a set of rules on how a vehicle should navigate itself on the road. For example, lane lines indicate which part of the road a vehicle can drive on in a certain direction. This research proposes a methodology that uses deep learning techniques to detect road arrows, road markings that show possible driving directions, on LIDAR derived images, and extract them as polyline vector shapefiles. The general workflow consists of (1) converting the LIDAR point cloud to images, (2) training and applying U-Net – a fully convolutional neural network, (3) creating masks from image segmentation results that have been transformed to fit the local coordinates, (4) extracting the polygons and polylines, and finally (5) exporting the vectors in shapefile format. The proposed methodology has shown promising results with object segmentation accuracies comparable with previous related works.

CERTAIN SOCIO-PSYCHOLOGICAL ASPECTS OF THE EFFECTIVENESS OF EDUCATION AND PROPAGANDA SAFE BEHAVIOR ON THE ROAD

2021 ◽  
pp. 107-110
Author(s):  
О.О. Марокко
Keyword(s):  
Road Safety ◽  
Life Support ◽  
Psychological Aspects ◽  
Life Support System ◽  
Entire Life ◽  
The Road ◽  
The Subject ◽  
On The Road ◽  
Social Advertising ◽  
The Relationship

В статье рассматриваются некоторые социально-психологические аспекты повышения эффективности социальной рекламы в области безопасности дорожного движения. Автором отмечено, что для достижения цели субъект пропаганды дол- жен осознать взаимосвязь между его индивидуальным поведением на дороге и надежностью всей системы жизнеобеспечения. The article discuses some of the socio-psychological aspects of increasing of social advertising in the field of road safety. The author notes that in order to achieve the goal, the subject of propaganda must aware of the relationship between his individual behavior on the road and the reliability of the entire life support system.

CHILDREN ROAD SAFETY AWARENESS: DESIGN AND DEVELOPMENT OF A ROAD SAFETY APPLICATION

2015 ◽  
Vol 77 (29) ◽  
Author(s):  
Nassiriah Shaari ◽  
Aeni Zuhana Saidin ◽  
Asmidah Alwi
Keyword(s):  
Road Safety ◽  
Web Application ◽  
Interactive Multimedia ◽  
Road Accidents ◽  
Design And Development ◽  
The Road ◽  
Usability Test ◽  
Safety Awareness ◽  
Safety Application ◽  
On The Road

Road safety campaigns and programs have been extensively introduced and implemented in Malaysia. However, their effectiveness is still being debated. Children especially will become the unfortunate victims of road accidents if they are unaware of the danger and precaution actions to be safe on the road. In response to that, this paper introduces an application as an alternative that inculcates road safety awareness to further support existing related programs and campaigns. Particularly, an interactive web application incorporating interactive multimedia elements has been designed and evaluated. Results on the usability test indicate a promising success and highlight aspects and issues that can be further focused for improvement and enhancement. 

scholarly journals The system of removing slush in passenger cars - a concept

2018 ◽  
Vol 19 (12) ◽  
pp. 217-220
Author(s):  
Michał Rubach ◽  
Konrad Waluś
Keyword(s):  
Atmospheric Pressure ◽  
Road Safety ◽  
Road Traffic ◽  
Dew Point ◽  
Passenger Cars ◽  
Dew Point Temperature ◽  
The Road ◽  
On The Road ◽  
Vehicle Movement ◽  
Temperature Surface

The appearance of slush on the road is determined by the intensity of precipitation, ambient temperature, surface and dew point temperature, atmospheric pressure and road traffic. The condition of slush (mixture of snow, ice, sand and chemicals such as salt) significantly affects the scope of road safety and the acceleration achieved in the driving processes. The agglomeration of slush in the space between the wheel and the wheel arches increases the resistance of the vehicle movement and increases the load on the suspension system and the steering. Excess snow and ice increases the risk of damage to these systems and may affect the steering and stability of the vehicle. The process of "deposition" of slush is particularly noticeable in environmental conditions with high humidity, and ambient and surface temperatures are below zero degrees Celsius. The article presents the idea of a system for removing slush from wheelhouse liners.

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