scholarly journals Infrared Sensors for Autonomous Vehicles

2018 ◽  
Author(s):  
Rajeev Thakur
Keyword(s):  
Autonomous Vehicles ◽  
Infrared Sensors

Safety driven intelligent autonomous vehicle for smart cities using IoT

2021 ◽  
Vol 17 (5) ◽  
pp. 563-582
Author(s):  
K. Balachander ◽  
C. Venkatesan ◽  
Kumar R.
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Time Data ◽  
Infrared Sensors ◽  
Content Type ◽  
Surrounding Environment ◽  
Sensor Information ◽  
Cloud Servers

Purpose Autonomous vehicles rely on IoT-based technologies to take numerous decisions in real-time situations. However, added information from the sensor readings will burden the system and cause the sensors to produce inaccurate readings. To overcome these issues, this paper aims to focus on communication between sensors and autonomous vehicles for better decision-making in real-time. The system has unique features to detect the upcoming and ongoing vehicles automatically without intervention of humans in the system. It also predicts the type of vehicle and intimates the driver. Design/methodology/approach The system is designed using the ATmega 328 P and ESP 8266 chip. Information from ultrasonic and infrared sensors are analyzed and updated in the cloud server. The user can access all these real-time data at any point of time. The stored information in cloud servers is used for integrating artificial intelligence into the system. Findings The real-time sensor information is used to predict the surrounding environment and the system responds to the user according to the situation. Practical implications The system is implemented on embedded platform with IoT technology. The sensor information is updated to the cloud using the Blynk application for the user in real time. Originality/value The system is proposed for smart cities with IoT technology where the user and the system are aware of the surrounding environment. The system is mainly concerned with the accuracy of sensors and the distance between the vehicles in real-time environment.

Rural Positioning Challenges for Connected and Autonomous Vehicles

2019 ◽  
Author(s):  
Joseph G. Walters ◽  
Xiaolin Meng ◽  
Chang Xu ◽  
Hao (Julia) Jing ◽  
Stuart Marsh
Keyword(s):  
Autonomous Vehicles

An Open Multi-Sensor Fusion Toolbox for Autonomous Vehicles

2020 ◽  
Vol E103.A (1) ◽  
pp. 252-264
Author(s):  
Abraham MONRROY CANO ◽  
Eijiro TAKEUCHI ◽  
Shinpei KATO ◽  
Masato EDAHIRO
Keyword(s):  
Sensor Fusion ◽  
Autonomous Vehicles

Fundamental Imaging System Analysis for Autonomous Vehicles

2018 ◽  
Vol 2018 (17) ◽  
pp. 105-1-105-10 ◽  
Author(s):  
Robin Jenkin ◽  
Paul Kane
Keyword(s):  
Autonomous Vehicles ◽  
System Analysis ◽  
Imaging System

Autonomous vehicles and freight traffic: towards better efficiency of road, rail or urban logistics?

2018 ◽  
Vol 58 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Péter Bucsky
Keyword(s):  
Autonomous Vehicles ◽  
Road Transport ◽  
Transport Sector ◽  
Future Directions ◽  
Car Industry ◽  
Level Of Automation ◽  
Current State ◽  
Freight Traffic ◽  
Levels Of Automation ◽  
Private Car

Abstract The freight transport sector is a low profit and high competition business and therefore has less ability to invest in research and development in the field of autonomous vehicles (AV) than the private car industry. There are already different levels of automation technologies in the transport industry, but most of these are serving niche demands and answers have yet to be found about whether it would be worthwhile to industrialise these technologies. New innovations from different fields are constantly changing the freight traffic industry but these are less disruptive than on other markets. The aim of this article is to show the current state of development of freight traffic with regards to AVs and analyse which future directions of development might be viable. The level of automation is very different in the case of different transport modes and most probably the technology will favour road transport over other, less environmentally harmful traffic modes.

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