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Empowering Urban Traffic Management: Elevated 3D LiDAR for Data Collection and Advanced Object Detection Analysis

Nawfal Guefrachi, Hakim Ghazzai, Ahmad Alsharoa

TL;DR

This work tackles the challenge of robust 3D object detection in complex urban traffic by leveraging elevated LiDAR and synthetic data generation. It presents a data-generation pipeline that combines experimental measurements with multiple simulators (Webots, Gazebo, CARLA, Blender) and a hybrid approach to align synthetic data with real-world scenarios, followed by a PV-RCNN-based detector tuned for dense urban point clouds. Through a Blender-driven case study, the authors demonstrate that PV-RCNN consistently outperforms SECOND on pedestrians and vehicles, using a large, richly labeled synthetic dataset. The findings highlight the importance of rich, varied training data and LiDAR’s privacy-preserving advantages for advancing urban surveillance and intelligent transportation systems. The approach offers a scalable path to improve 3D perception in smart cities where real-world data are scarce or hard to collect.

Abstract

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our experimental analysis, we firstly simulate various 3D point cloud traffic-related objects. Then, we use this dataset as a basis for training and evaluating our 3D object detection models, in identifying and monitoring both vehicles and pedestrians in simulated urban traffic environments. Next, we fine tune the Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making it more suited to handle and understand the massive volumes of point cloud data generated by our urban traffic simulations. Our results show the effectiveness of the proposed solution in accurately detecting objects in traffic scenes and highlight the role of LiDAR in improving urban safety and advancing intelligent transportation systems.

Empowering Urban Traffic Management: Elevated 3D LiDAR for Data Collection and Advanced Object Detection Analysis

TL;DR

This work tackles the challenge of robust 3D object detection in complex urban traffic by leveraging elevated LiDAR and synthetic data generation. It presents a data-generation pipeline that combines experimental measurements with multiple simulators (Webots, Gazebo, CARLA, Blender) and a hybrid approach to align synthetic data with real-world scenarios, followed by a PV-RCNN-based detector tuned for dense urban point clouds. Through a Blender-driven case study, the authors demonstrate that PV-RCNN consistently outperforms SECOND on pedestrians and vehicles, using a large, richly labeled synthetic dataset. The findings highlight the importance of rich, varied training data and LiDAR’s privacy-preserving advantages for advancing urban surveillance and intelligent transportation systems. The approach offers a scalable path to improve 3D perception in smart cities where real-world data are scarce or hard to collect.

Abstract

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our experimental analysis, we firstly simulate various 3D point cloud traffic-related objects. Then, we use this dataset as a basis for training and evaluating our 3D object detection models, in identifying and monitoring both vehicles and pedestrians in simulated urban traffic environments. Next, we fine tune the Point Voxel-Region-based Convolutional Neural Network (PV-RCNN) architecture, making it more suited to handle and understand the massive volumes of point cloud data generated by our urban traffic simulations. Our results show the effectiveness of the proposed solution in accurately detecting objects in traffic scenes and highlight the role of LiDAR in improving urban safety and advancing intelligent transportation systems.
Paper Structure (22 sections, 6 figures, 1 table)

This paper contains 22 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. LiDAR-enabled Tasks and Data Scarcity
  3. Data Collection Strategies
  4. Experimental Approach
  5. Simulator Approach
  6. Webots
  7. Gazebo
  8. Carla
  9. Blender for Data Generation
  10. Hybrid Approach: Adjustable Simulator for Real-World Scenarios
  11. Data Generation
  12. Urban Environment Simulation and Scenarios
  13. Objects Animations
  14. Scenarios Specifications
  15. Data Collection
  16. ...and 7 more sections

Figures (6)

  • Figure 1: General workflow of object monitoring.
  • Figure 2: Experimental setup for pedestrian detection, conducted in the OIWS lab at MST.
  • Figure 3: Experimental setup for vehicle detection using Jetbots, conducted in the OIWS lab at MST.
  • Figure 4: This work, conducted in the OIWS lab at MST, involved collecting LiDAR data through a steering wheel in a simulated environment.
  • Figure 5: Screenshot of a simulated scene from Blender.
  • ...and 1 more figures