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Autonomous cars have revolutionized transportation, prioritizing safety, efficiency, and eco-friendliness. Stereo vision, mimicking human perception, aids in lane detection, space assessment, and obstacle recognition. Driven by precise disparity maps, autonomous navigation algorithms are based on depth perception and object recognition. Complete autonomy is yet hampered by resource-intensive stereo vision. Edge computing, a paradigm shift, allows local data processing, reducing server reliance. Integration of stereo vision, edge computing, and advanced algorithms like improved Quadruple Sparse Census Transform (iQSCT) marks a significant leap in autonomous vehicles. It tackles computing limitations, enhancing accuracy in vital tasks like obstacle detection and lane identification, ensuring safer, more efficient mobility, and paving the way for future navigation advancements.

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Stereo-Edge Computation for Autonomous Driving Through Enhanced Environmental Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • R. Sadhana,
  • S. Rajesh Kannan,
  • M. Angelin Ponrani,
  • P. Ezhilarasi

摘要

Autonomous cars have revolutionized transportation, prioritizing safety, efficiency, and eco-friendliness. Stereo vision, mimicking human perception, aids in lane detection, space assessment, and obstacle recognition. Driven by precise disparity maps, autonomous navigation algorithms are based on depth perception and object recognition. Complete autonomy is yet hampered by resource-intensive stereo vision. Edge computing, a paradigm shift, allows local data processing, reducing server reliance. Integration of stereo vision, edge computing, and advanced algorithms like improved Quadruple Sparse Census Transform (iQSCT) marks a significant leap in autonomous vehicles. It tackles computing limitations, enhancing accuracy in vital tasks like obstacle detection and lane identification, ensuring safer, more efficient mobility, and paving the way for future navigation advancements.