Real-time image processing for autonomous vehicles: a GPU-accelerated approach in ubuntu

Abstract

Autonomous vehicles heavily rely on real-time image processing for navigation and decision-making. However, performing real-time processing of high-resolution images presents a significant computational challenge that might hinder the advancement of autonomous vehicle technology. Computational requirements associated with image processing could act as a constraining factor in the advancement of autonomous vehicles. This study introduces an innovative GPU-accelerated method tailored for real-time image processing on Ubuntu, specifically designed for autonomous vehicle tasks. The approach capitalizes on the CUDA programming paradigm to exploit the parallel processing capabilities of NVIDIA GPUs, resulting in substantial performance boosts when compared to typical CPU-centric techniques. The findings of this study carry substantial implications for the evolution of autonomous vehicles, facilitating quicker and more effective image processing to enhance safety and operational efficiency. Additionally, they highlight the potential of GPU-accelerated image processing in the domain of self-driving cars by facilitating faster and more effective management of visual information.