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Cyclist Orientation Estimation Using LiDAR Data.

Hyoungwon Chang1, Yanlei Gu1, Igor Goncharenko1

  • 1College of Information Science and Engineering, Ritsumeikan University, 1-1-1, Noji-higashi, Kusatsu 525-8577, Shiga, Japan.

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Summary
This summary is machine-generated.

Predicting cyclist behavior is key for autonomous vehicles. This study uses LiDAR data and deep learning to estimate cyclist body and head orientation, finding 3D point clouds are more effective than 2D images.

Keywords:
LiDARcyclistdeep neural networkorientation estimation

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Area of Science:

  • Computer Vision
  • Robotics
  • Transportation Engineering

Background:

  • Autonomous vehicles require accurate cyclist behavior prediction for safe navigation.
  • Cyclist body and head orientation provide critical cues for predicting movement and intentions.
  • Existing methods often lack robust cyclist orientation estimation capabilities.

Purpose of the Study:

  • To develop and evaluate deep learning models for estimating cyclist body and head orientation using LiDAR data.
  • To compare the effectiveness of 2D image-based versus 3D point cloud-based approaches for this task.
  • To determine the optimal use of LiDAR sensor data features (reflectivity, ambient, range) for cyclist orientation estimation.

Main Methods:

  • Utilized a ResNet50 convolutional neural network architecture for orientation classification.
  • Developed two distinct methods: one using 2D LiDAR data representations (reflectivity, ambient, range) and another using 3D point cloud data.
  • Created a novel cyclist dataset containing diverse body and head orientations.

Main Results:

  • The 3D point cloud-based method significantly outperformed the 2D image-based method in cyclist orientation estimation.
  • Within the 3D method, using LiDAR reflectivity information yielded more accurate results than using ambient information.
  • The developed dataset facilitated robust training and evaluation of the orientation estimation models.

Conclusions:

  • 3D point cloud data derived from LiDAR sensors offers superior performance for cyclist orientation estimation in autonomous driving scenarios.
  • LiDAR reflectivity is a more informative feature than ambient data for this specific task.
  • The proposed deep learning approach enhances the safety and reliability of autonomous vehicles by improving cyclist behavior prediction.