GPS tracking system for autonomous vehicles

Zein, Yassine; Darwiche, Mohamad; Mokhiamar, Ossama

doi:10.1016/j.aej.2017.12.002

Cited by 45 publications

(16 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…-The Arduino Mega card, which receives the signals from the air quality sensors and sends the appropriate signals to the servo motors using a mobile software (Zein et al, 2018). Using inverse kinematic equations described in (Crenganis et al, 2019) to get the joint variables that would put the sprinkler in the desired location.…”

Section: Methodsmentioning

confidence: 99%

Using robots to improve indoor air quality and reduce COVID-19 exposure

Abdallah¹,

Nasr²

2021

JART

View full text Add to dashboard Cite

Air pollution is the world’s largest environmental health risk, accounting yearly for about 8 million deaths. Most of the people spend 80% to 90% of their time indoors. Therefore, Indoor Air Quality (IAQ) has a direct impact on overall health. In this paper, a robot, equipped with sensors for measuring IAQ parameters such as temperature, relative humidity, CO2, Particulate Matters and Volatile Organic Compounds (VOC) is used to measure these parameters and compare the measured values to the pre-set permissible levels of concentrations of such parameters. If one (or more) of the measured values exceed the pre-set threshold values, the measured values are communicated to the user via a mobile application and the robot will take a corrective action by disinfecting the place using a sterilizing solution. Validation of the methodology was verified at an educational institute in Alexandria, Egypt.

show abstract

Section: Methodsmentioning

confidence: 99%

Using robots to improve indoor air quality and reduce COVID-19 exposure

Abdallah¹,

Nasr²

2021

JART

View full text Add to dashboard Cite

show abstract

“… GPS Sensors: The GPS sensor receives geolocation and timing information from a satellite-based navigation system. If you have an uninterrupted sight of four or more satellites, your position can only be indicated by a few meters [45]. While it is necessary to move from point A to point B, it will be inadequate on its own due to weather conditions.…”

Section: A Sensor Fusion For Autonomous Drivingmentioning

confidence: 99%

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

et al. 2021

View full text Add to dashboard Cite

Blockchain is an underlying technology for securing many real-time applications and their data. The automobile is one such sector in which auto-manufacturers are looking forward to accepting the advantages of distributed ledger technology in autonomous vehicles or systems and improving their products, customer satisfaction, and other valuable experiences. This work aims to find the significance of blockchain technology in Autonomous Vehicles, including Autonomous Electric Vehicles (AEV), Autonomous Underwater Vehicles (AUV), Autonomous Guided Vehicles (AGV), Autonomous Aerial Vehicles (AAeV), and Autonomous Driving. In this work, a comparative analysis of blockchain-integrated autonomous vehicle systems is explored to identify the present scenario and futuristic challenges. In addition to blockchain technology, the uses and importance of sensors, architectures and infrastructure requirements, vehicle types, driving modes, vehicles target and tracking approaches, intelligent contracts, intelligent data handling, and industry-specific use cases are also explored. This study is based on the exploration of recent technologies and practices. As autonomous vehicles are expected to be the future of intelligent transportation, this paper surveys recent advances in autonomous vehicles and systems and how blockchain can help in improving user experiences and improving industry practices. Finally, limitations of work, future research directions, and challenges associated with different autonomous vehicles and systems are presented.

show abstract

“…SPP is considered for positioning performance of single-frequency users and is referred to as single point positioning using pseudo-range measurements and navigation parameters. For KPS/GPS SPP simulation, we use only the simulated P1 code measurements, as presented in Equation (1). The error sources in SPP simulation include satellite ephemeris, satellite clock, atmospheric effects (ionosphere and troposphere), and observation noises.…”

Section: Observation Simulationmentioning

confidence: 99%

“…A global navigation satellite system (GNSS) refers to one or more satellite constellations providing a positioning, navigation, and timing (PNT) service to users around the world. GNSS is becoming an essential part of human life, which is used in many applications such as autonomous driving [1], surveying [2], precise time transfer [3], and mobile phones [4]. It even can contribute to many scientific fields [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of the Korean Positioning System Using Observation Simulation

Choi

Roh

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

The Korean government has a plan to build a new regional satellite navigation system called the Korean Positioning System (KPS). The initial KPS constellation is designed to consist of seven satellites, which include three geostationary Earth orbit (GEO) satellites and four inclined geosynchronous orbit (IGSO) satellites. KPS will provide an independent positioning, navigation, and timing (PNT) service in the Asia-Oceania region and can also be compatible with GPS. In the simulation for KPS, we employ 24 GPS as designed initially and 7 KPS satellites. Compared to the true orbit that we simulated, the averaged root mean square (RMS) values of orbit-only signal-in-space ranging errors (SISRE) are approximately 4.3 and 3.9 cm for KPS GEO and IGSO. Two different positioning solutions are analyzed to demonstrate the KPS performance. KPS standard point positioning (SPP) errors in the service area are about 4.7, 3.9, and 7.1 m for east (E), north (N), and up (U) components, respectively. The combined KPS+GPS SPP accuracy can be improved by 25.0%, 31.8%, and 35.0% compared to GPS in E, N, and U components. The averaged position errors for KPS kinematic precise point positioning (KPPP) are less than 10 cm. In the fringe of the KPS service area, however, the position RMS errors can reach about 40 cm. Unlike KPS, GPS solutions show high positioning accuracy in the KPS service area. The combined KPS+GPS can be improved by 28.7%, 27.1%, and 30.5% compared to GPS in E, N, and U components, respectively. It is noted that KPS can provide better performance with GPS in the Asia-Oceania region.

show abstract

GPS tracking system for autonomous vehicles

Cited by 45 publications

References 2 publications

Using robots to improve indoor air quality and reduce COVID-19 exposure

Using robots to improve indoor air quality and reduce COVID-19 exposure

Blockchain and Autonomous Vehicles: Recent Advances and Future Directions

Performance Analysis of the Korean Positioning System Using Observation Simulation

Contact Info

Product

Resources

About