Design and Implementation of Advanced Automatic Driving System using Raspberry Pi

Mariaraja, P.; Manigandan, T.; Arun, S.; Sundaram, R.M.D.c; Dhenesh, K.

doi:10.1109/icces48766.2020.9138082

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…Es capaz de interceptar el tráfico en un segmento de red, capturar contraseñas y realizar escuchas clandestinas en varios protocolos comunes. Además, se uso para todas las pruebas Advanced IP Scanner [21]…”

Section: Materiales Y M éTodosunclassified

Vulnerability analysis of a Raspberry Pi in a simulated environment

Enriquez,

Arreaga

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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IoT devices are currently part of our environment, collecting confidential data and information. Therefore, it is important to know what vulnerabilities are present in the devices that are being used. This study shows that Raspberry Pi devices do not have a defense system that detects malicious or virus-infected files. This is one of the reasons why they are vulnerable to various attacks. To test this, three different attacks-Man In The Middle (MITM), Backdoor and Denial of Service (DoS)-were performed on the Raspberry Pi device using a virtual machine with the Kali Linux operating system. It is shown that Raspberry Pi with the Raspbian operating system is vulnerable to some of the test attacks. The time it takes for the attacker to execute the man-in-the-middle and backdoor attacks is 1 s. In the case of a Denial of Service (DoS) attack, it takes 60 seconds to execute. It was shown that an MITM attack takes much longer to prepare, approximately 15 min, than a DoS attack that takes 8 min.

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Section: Materiales Y M éTodosunclassified

Vulnerability analysis of a Raspberry Pi in a simulated environment

Enriquez,

Arreaga

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

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“…Jain et al [9] introduced a self-driving car model based on convolutional neural networks (CNNs) implemented on Raspberry Pi and Arduino platforms. Real time implementation on lane detection using Image processing and raspberry pi is reported in [10,11].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Based Autonomous Road Lane Detection and Navigation System for Vehicles

Dubey,

Tarte,

Talatule

et al. 2024

Preprint

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Lane detection is a foundational technology for autonomous driving systems. It involves identifying the boundaries of lanes on the road and ensuring the vehicle stays within these boundaries. Accurate lane detection is crucial for safety, navigation and traffic management. This paper presents an artificial intelligent based autonomous road lane detection and navigation system for vehicles. The system can detect and analyze road lanes accurately and efficiently in real-time, with frame rates of up to 17 FPS. By utilizing image processing techniques, the system can identify the location and boundaries of road lanes and obstacles on the road and provide accurate and reliable navigation guidance to the driver. The system is integrated with sensors and actuators to provide comprehensive autonomous navigation. The efficiency of the proposed system is demonstrated using accuracy of lane detection on straight and curve lanes covering frames per second and motor of the speed as parameters for assessment. Results show that the proposed system detects lane as entire lane detection, partial lane detection and no detection status on different settings.

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