Self-Driving Car Using Image Processing

Autonomous cars represent a new mode of transportation using hardware and software to facilitate autonomous driving without human intervention. This is a design document for the creation of a low-cost autonomous vehicle prototype based on the Raspberry Pi 3 as the core processor. The system combines lane detection, traffic light identification, and traffic sign recognition into a single multi-threaded pipeline that facilitates real-time multi-task perception. Experimental testing demonstrates that the prototype attains a mean accuracy of 87%, a processing rate of 13–14 frames per second, and a mean decision time of approximately 105 ms in outdoor and indoor environments. In contrast to earlier Raspberry Pi-based prototypes that executed tasks sequentially or merely followed lanes, the system exhibits the novelty of stable multi-task perception with limited resources. The findings indicate the potential and limitations of embedded systems in autonomous navigation, with possible future applications in transportation, industrial automation, and surveillance.

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Publication Details

Journal
Journal of ISMAC
Published
2025-10-07
DOI
https://doi.org/10.36548/jismac.2025.3.004
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
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article

Self-Driving Car Using Image Processing

Journal of ISMAC
Genomics and Phylogenetic Studies
article

Self-Driving Car Using Image Processing

article en

Abstract

Autonomous cars represent a new mode of transportation using hardware and software to facilitate autonomous driving without human intervention. This is a design document for the creation of a low-cost autonomous vehicle prototype based on the Raspberry Pi 3 as the core processor. The system combines lane detection, traffic light identification, and traffic sign recognition into a single multi-threaded pipeline that facilitates real-time multi-task perception. Experimental testing demonstrates that the prototype attains a mean accuracy of 87%, a processing rate of 13–14 frames per second, and a mean decision time of approximately 105 ms in outdoor and indoor environments. In contrast to earlier Raspberry Pi-based prototypes that executed tasks sequentially or merely followed lanes, the system exhibits the novelty of stable multi-task perception with limited resources. The findings indicate the potential and limitations of embedded systems in autonomous navigation, with possible future applications in transportation, industrial automation, and surveillance.

Journal of ISMACVol. 7(3)
Cochin University of Science and Technology (IN), Electronics Design (Estonia) (EE)
Openalex Percentile: Top 46%
Genomics and Phylogenetic Studies
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Self-Driving Car Using Image Processing · Journal of ISMAC (2025) | TGRS Research Map | TGRS