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Enhanced Perception for Autonomous Driving Using Semantic and Geometric Data Fusion.

Horatiu Florea1, Andra Petrovai1, Ion Giosan1

  • 1Image Processing and Pattern Recognition Research Center, Computer Science Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.

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Summary

This study introduces a novel real-time, 360-degree enhanced perception system for autonomous driving. It fuses LiDAR and RGB camera data for improved object detection and classification, enhancing vehicle safety.

Keywords:
3D object detectionautonomous drivingdeep learningenvironment perceptionlow-level geometry and semantic fusionsemantic and instance segmentation

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Area of Science:

  • Computer Vision
  • Robotics
  • Autonomous Systems

Background:

  • Environment perception is critical for autonomous driving but remains a challenge.
  • Multi-modal sensing improves performance by leveraging complementary sensor properties.
  • Data fusion in multi-modal systems introduces complexity, impacting quality and latency.

Purpose of the Study:

  • To develop a real-time, 360-degree enhanced perception system for autonomous vehicles.
  • To address the challenges of data fusion in multi-modal perception systems.
  • To improve the robustness, detection, and classification capabilities of autonomous driving perception.

Main Methods:

  • Low-level fusion of LiDAR 3D point clouds and semantic information from multiple RGB cameras.
  • Utilizing deep learning for 2D semantic, instance, and panoptic segmentation.
  • Employing a voxel-based solution for 3D point cloud segmentation.
  • Point-to-image projection for fusing 2D semantic data with 3D point clouds.

Main Results:

  • Achieved a semantically enhanced 3D point cloud through multi-modal fusion.
  • Demonstrated improved range coverage, detection, and classification quality.
  • Enhanced robustness in perception tasks.
  • Successfully refined 3D detection and object classification.

Conclusions:

  • The developed perception system provides a semantically enhanced 3D point cloud for downstream tasks.
  • The multi-modal, multi-sensor approach significantly boosts perception capabilities.
  • The system was successfully integrated into an autonomous vehicle software stack.