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SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads

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This study introduces a vision-based system for vehicle localization on roads without lane lines, using RGB images. It fuses semantic segmentation and depth estimation to determine road width and fence-to-fence distance, enhancing driving safety.

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Advanced Driver Assistance Systems (ADAS)autonomous drivingcomputer visiondeep learningfusion architecturemonocular depth estimationscene understandingsemantic segmentationsituational awareness

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Area of Science:

  • Computer Vision
  • Autonomous Driving Systems

Background:

  • Traditional lane departure warning systems require visible lane lines.
  • Many real-world driving scenarios, especially secondary roads and urban streets, lack clear lane markings.

Purpose of the Study:

  • To develop a vision-based method for vehicle localization on roads without lane lines.
  • To enable accurate road width and fence-to-fence distance estimation using only RGB imagery.

Main Methods:

  • Fusing semantic segmentation and monocular depth estimation to create a semantic 3D point cloud.
  • Retaining road points and roadside structures (fences/walls) for spatial analysis.
  • Computing road width and fence-to-fence distance along the planned trajectory.

Main Results:

  • Successfully demonstrated vehicle localization and road geometry estimation in environments lacking lane lines.
  • Quantitative evaluation on Munich street images with road-fence structures.
  • Qualitative validation on the Cityscapes dataset (Stuttgart sequence) in a standard urban setting.

Conclusions:

  • The proposed method effectively locates vehicles on roads without lane markings.
  • The system provides complementary road width and fence-to-fence distance measurements.
  • Open-source software is released to benefit the research community.