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Related Experiment Video

Updated: Jun 26, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR

Harin Jang1, Taehyun Kim1, Kyungjae Ahn1

  • 1Graduate School of Automotive Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea.

Sensors (Basel, Switzerland)
|May 11, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces dynamic occupancy grid maps (DOGMs) enhanced with object classification by fusing camera and LiDAR data. This improves perception for autonomous vehicles in complex urban environments.

Keywords:
autonomous vehiclesoccupancy grid mapparticle filterssemantic grid mapsensor fusion

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Area of Science:

  • Robotics and Autonomous Systems
  • Sensor Fusion
  • Artificial Intelligence

Background:

  • Dynamic Occupancy Grid Maps (DOGMs) are crucial for representing object position and velocity in robotics and autonomous driving.
  • Current 3D Light Detection and Ranging (LiDAR) based DOGMs lack object classification capabilities, limiting their application scope.
  • Integrating camera data with LiDAR is essential to overcome the limitations of single-sensor systems.

Purpose of the Study:

  • To develop a novel deep learning-based sensor fusion technique for enhancing DOGMs with object class information.
  • To improve the reliability and scope of perception systems for autonomous vehicles.
  • To leverage Dempster-Shafer evidence theory for incorporating class information and uncertainty into DOGMs.

Main Methods:

  • Implemented a deep learning model for camera-LiDAR sensor fusion as input for DOGMs.
  • Developed update rules based on Dempster-Shafer evidence theory to integrate object class and uncertainty.
  • Investigated two occupancy probability assignment models (edge vs. entire bounding box) for velocity estimation accuracy analysis.
  • Utilized the nuScenes dataset for performance evaluation.

Main Results:

  • The fused sensor approach successfully incorporated object class information into DOGMs, expanding their applicability.
  • Unclassified LiDAR points contributed to map formation, enhancing overall perception reliability.
  • Analysis of velocity estimation accuracy was performed using different occupancy probability assignment models.
  • The developed technique demonstrated effective performance on the nuScenes dataset.

Conclusions:

  • The enhanced DOGM with object class information provides richer perception data for autonomous vehicles.
  • This advancement is critical for enabling safer and more efficient navigation in complex urban driving scenarios.
  • The fusion of deep learning, sensor data, and evidence theory offers a robust approach to environmental perception.