Effect of Vehicle to Vehicle and Vehicle to Infrastructure Communication Systems on Transportation Safety

2006 ◽  
Author(s):  
S. Diwan ◽  
B. Dalla Chiara ◽  
F. Deflorio
Keyword(s):  
Communication Systems ◽  
Transportation Safety ◽  
Vehicle To Vehicle ◽  
Vehicle To Infrastructure ◽  
Vehicle To Infrastructure Communication

scholarly journals Design and Implementation of V2V and V2I Communication Systems using ML based Li-Fi technology

2021 ◽  
Author(s):  
Manimegaai C T ◽  
kali muthu ◽  
sabitha gauni
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Machine Learning Techniques ◽  
Light Emitting ◽  
Design And Implementation ◽  
Vehicle To Vehicle ◽  
The People ◽  
Learning Techniques ◽  
Vehicle To Infrastructure ◽  
Vehicle To Infrastructure Communication

Abstract These days population are taking a risk in their drive and in no time dangers are happening, and loosing lives by doing tiny wrongs when on drive near restricted zones. To escape these accidents to make population risk free traffic department are introducing signboards. But then again with the ignorance of the people, dangers are happening again, so “Li-Fi technology” is being used here to decrease the count of accidents. The transmission takes place with the help of LEDs (Light Emitting Diodes).Text, audio and video can also be transmitted with the help of this li-fi. The transmission is done when the light turns on and off. When this is compared to Wi-Fi it has many advantages like this light is not harmful to human body. The Transmission takes place in the form of zeroes and ones. Therefore to avoid accidents we suggested an intelligent, adaptable, and efficient model that utilizes Machine Learning techniques. The proposed system helps in vehicle to vehicle and vehicle to Infrastructure communication systems.

scholarly journals Arduino-Based Solution for In-Car-Abandoned Infants' Detection Remotely Managed by Smartphone Application

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Paolo Visconti ◽  
Roberto De Fazio ◽  
Paolo Costantini ◽  
Simone Miccoli ◽  
Donato Cafagna
Keyword(s):  
Control System ◽  
Communication Systems ◽  
Smartphone Application ◽  
Mobile App ◽  
Research Groups ◽  
Software Applications ◽  
Vehicle To Vehicle ◽  
Safety And Reliability ◽  
Vehicle To Infrastructure ◽  
Vehicle To Infrastructure Communication

This manuscript deals with V2V/V2I (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) communication systems developed for smart city applications, with the aim to provide new services and tools for making driving safer and improving the human lifestyle. The considered systems can be supported by suitable software applications for making the services more accessible. In this context, research groups and automotive companies are currently developing systems against children abandonment in unattended vehicles and are installing them on new car models. In this paper, an innovative Arduino-based control system against children abandonment in cars is described. It introduces new functionalities respect to systems reported in literature or already on the market, in order to improve safety and reliability. The proposed system integrates a mobile app, which gives the possibilities of receiving alert or status messages, along with images directly acquired from car cockpit. In addition, the app allows to remotely control several car functionalities, such as horn activation, windows lowering and doors locking/unlocking.

Social interactions in traffic: The effect of external factors

2018 ◽  
Vol 62 (1) ◽  
pp. 97-101 ◽  
Author(s):  
Tanja Stoll ◽  
Nadine-Rebecca Strelau ◽  
Martin Baumann
Keyword(s):  
Social Interactions ◽  
Cooperative Behavior ◽  
Lane Change ◽  
Lane Changing ◽  
Vehicle To Vehicle ◽  
Driving Task ◽  
Vehicle To Infrastructure ◽  
Underlying Mechanisms ◽  
Intention To Change ◽  
Vehicle To Infrastructure Communication

Social interactions were always part of the driving task, but the introduction of vehicle-to-vehicle and vehicle-to-infrastructure communication opens up new possibilities for cooperative interactive driving. It enables drivers to coordinate their maneuvers cooperatively with other involved traffic users. To ensure drivers’ acceptance of such automated systems it is necessary to understand the underlying mechanisms of human cooperation in traffic. In this experiment, we investigate potential influencing factors on the willingness to behave cooperatively in a lane change situation on a highway. In a video-based study, we manipulated the costs of cooperation, the situation’s criticality for the lane-changing vehicle and the way in which the intention to change the lane was indicated. Cooperative behavior is influenced by lower costs, a higher situation’s criticality and by signaling the intention to lane change. These results offer insights that may be used in the developing process of Human Machine Interfaces (HMI) for cooperatively interacting vehicles

After you: Merging at Highway On-Ramps

2020 ◽  
Vol 64 (1) ◽  
pp. 1105-1109
Author(s):  
Tanja Stoll ◽  
Lucas Weihrauch ◽  
Martin Baumann
Keyword(s):  
Influencing Factors ◽  
The Other ◽  
Lane Change ◽  
Lane Changing ◽  
Vehicle To Vehicle ◽  
Human Machine Interfaces ◽  
Human Driver ◽  
Vehicle To Infrastructure ◽  
Intention To Change ◽  
Vehicle To Infrastructure Communication

Vehicle-to-vehicle and vehicle-to-infrastructure communication offer new possibilities for cooperatively interactive driving. It enables vehicles to carry out maneuvers cooperatively with other vehicles. However, these maneuvers have to be predictable and understandable to a human driver to prevent the driver from intervening with the automation. In a video-based study, we investigated potential influencing factors on the willingness to behave cooperatively in an on-ramp situation on a highway: the situation’s criticality for the lane-changing vehicle, the way the intention to change the lane was indicated and the scope of action. Moreover, we asked participants to rate their perceived criticality. Participants preferred to change lanes to the left or decelerate to let the other vehicle merge in front of them. If a lane change was not possible, participants rated the situation as more critical. These results are useful for the developing process of human-machine interfaces for cooperatively interacting vehicles.

scholarly journals Improving Communication System for Vehicle-to-Everything Networks by Using 5G Technology

2021 ◽  
Author(s):  
Tarik Adnan Almohamad ◽  
Muhammet Tahir Güneşer ◽  
Mohd Nazri Mahmud ◽  
Cihat Şeker
Keyword(s):  
Communication Systems ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Wireless Communication Systems ◽  
Identification Schemes ◽  
Vehicle To Vehicle ◽  
Learning Techniques ◽  
Vehicle To Infrastructure

Next-generations of wireless communication systems (5G scheme & beyond) are rapidly evolving in the contemporary life. These schemes could propose vital solutions for many existing challenges in various aspects of our lives, eventually to ensure stable communications. Such challenges are even greater when it comes to address ubiquitous coverage and steady interconnection performance in fast mobile vehicles (i.e., trains or airplanes) where certainly blind spots exist. As an early initiative, the Third Generation Partnership Project (3GPP) has proposed a regulation for Long Term Evolution (LTE)-based Vehicle-to-Everything (V2X) network in order to offer solid solutions for V2X interconnections. V2X term should comprise the following terminologies: vehicle-to-vehicle (V2V), vehicle-to-network (V2N) communications, vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P). Superior V2X communications have a promising potential to improve efficiency, road safety, security, the accessibility of infotainment services (any service of user-interface exists inside a vehicle). In this chapter, the aforementioned topics will be addressed. In addition, the chapter will open the door on investigating the role of wireless cooperative and automatic signal identification schemes in V2X networks, and shedding light on the machine learning techniques (i.e, Support Vector Machines (SVMs), Deep Neural Networks (DNNs)) when they meet with the next-generations of wireless networks.

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