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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

Summary

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 vehicles occupancy grid map particle filters semantic grid map sensor 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.