Ensuring road safety and efficient traffic management is of paramount importance in urban areas. The advent of 3D LiDAR technology offers an opportunity to enhance these aspects by accurately identifying vehicles and pedestrians in complex road environments. This thesis proposal outlines a collaborative research project between Comark srl, Datamind srl, and the University of Udine, aimed at developing algorithms for 3D point cloud segmentation, sensor data logging, and vehicle and pedestrian classification. The primary goal is to improve road safety and traffic management through advanced computer vision techniques.
The primary objectives of this collaborative research project are as follows:
To review and analyze the state-of-the-art 3D LiDAR technologies, computer vision techniques, and existing algorithms for vehicle and pedestrian identification in road environments.
To develop novel algorithms for 3D point cloud segmentation, sensor data logging, and vehicle and pedestrian classification using 3D LiDAR data.
To implement and validate the developed algorithms on real-world road data collected in urban environments.
To assess the impact of the proposed technology on road safety and traffic management, and to provide recommendations for its integration into transportation systems.
A comprehensive review of existing literature will be conducted to establish the current state of knowledge regarding 3D LiDAR technology and computer vision techniques for vehicle and pedestrian identification in road environments. This section will cover relevant technological advancements, case studies, and gaps in existing research.
Technology Review: Evaluate the current state of 3D LiDAR technology and sensor capabilities for vehicle and pedestrian detection in complex road environments.
Algorithm Development: Develop novel algorithms for 3D point cloud segmentation, sensor data logging, and vehicle and pedestrian classification using 3D LiDAR data.
Data Collection and Validation: Collect real-world road data in urban environments and implement the developed algorithms to validate their accuracy and reliability.
Performance Assessment: Evaluate the impact of the proposed technology on road safety and traffic management through empirical experiments and case studies.
This collaborative research project is expected to yield the following results:
An in-depth review of state-of-the-art 3D LiDAR technologies and computer vision techniques for vehicle and pedestrian identification in road environments.
Novel algorithms for 3D point cloud segmentation, sensor data logging, and vehicle and pedestrian classification using 3D LiDAR data.
Empirical evidence demonstrating the accuracy and reliability of the developed algorithms in real-world urban road environments.
Recommendations for integrating the proposed technology into transportation systems to improve road safety and traffic management.
This collaborative research project has significant implications for road safety and traffic management in urban areas. By leveraging 3D LiDAR technology and advanced computer vision techniques, we can enhance the accuracy and efficiency of vehicle and pedestrian identification, leading to improved road safety and more effective traffic management.
The collaborative research project will be conducted over a period of 6m, with the following approximate timeline:
This collaborative research project aims to harness the power of 3D LiDAR technology to enhance vehicle and pedestrian identification in complex road environments. The research strives to contribute to the improvement of road safety and traffic management in urban areas through innovative algorithms and advanced computer vision techniques. Ultimately, this work can lead to safer and more efficient transportation systems, benefiting society as a whole.