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A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems.

Tao Wu¹, Jun Hu¹, Lei Ye¹

¹College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

Sensors (Basel, Switzerland)

|February 10, 2021

Summary

This summary is machine-generated.

This study introduces a novel pedestrian detection algorithm for autonomous vehicles using score fusion from two Light Detection and Ranging (LiDAR) systems. The method achieves real-time, accurate pedestrian detection by integrating features and employing Bayesian fusion.

Keywords:

autonomous vehicles pedestrian detection sensor fusion sliding window

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Area of Science:

Robotics and Autonomous Systems
Computer Vision
Sensor Fusion

Background:

Pedestrian detection is critical for autonomous vehicle navigation.
Multisensor fusion enhances detection performance but presents integration challenges.

Purpose of the Study:

To develop a score fusion-based pedestrian detection algorithm by integrating data from two LiDAR systems.
To improve real-time detection accuracy and efficiency for autonomous vehicles.

Main Methods:

Implemented a two-stage object-detection pipeline (proposal and classification) for each LiDAR.
Utilized Bayesian rule for score fusion of classifier outputs.
Incorporated central points density and location features (point cloud density and height distribution) to enhance proposal performance and reduce false alarms.

Main Results:

Achieved highly accurate pedestrian detection results in real-time.
Demonstrated effectiveness on the KITTI dataset and a self-built dataset.
Validated the algorithm's accuracy, efficiency, and flexibility across different modalities.

Conclusions:

The proposed score fusion method effectively integrates dual-LiDAR data for robust pedestrian detection.
The algorithm balances accuracy, efficiency, and flexibility, suitable for real-world autonomous driving applications.