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LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving
Technical Paper
2020-01-0136
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
Autonomous vehicles are currently a subject of great interest and there is heavy research on creating and improving algorithms for detecting objects in their vicinity. A ROS-based deep learning approach has been developed to detect objects using point cloud data. With encoded raw light detection and ranging (LiDAR) and camera data, several basic statistics such as elevation and density are generated. The system leverages a simple and fast convolutional neural network (CNN) solution for object identification and localization classification and generation of a bounding box to detect vehicles, pedestrians and cyclists was developed. The system is implemented on an Nvidia Jetson TX2 embedded computing platform, the classification and location of the objects are determined by the neural network. Coordinates and other properties of the object are published on to various ROS topics which are then serviced by visualization and data handling routines. Performance of the system is scrutinized with regards to hardware capability, software reliability, and real-time performance. The final product is a mobile-platform capable of identifying pedestrians, cars, trucks and cyclists.
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Authors
- Aarron Younan - University of Windsor
- Brad Sato - University of Windsor
- Abdul El-Kadri - University of Windsor
- Selwan Nissan - University of Windsor
- Kemal Tepe - University of Windsor
- Ismail Hamieh - National Research Council Canada
- Ryan Myers - National Research Council Canada
- Hisham Nimri - National Research Council Canada
- Taufiq Rahman - National Research Council Canada
Topic
Citation
Hamieh, I., Myers, R., Nimri, H., Rahman, T. et al., "LiDAR and Camera-Based Convolutional Neural Network Detection for Autonomous Driving," SAE Technical Paper 2020-01-0136, 2020, https://doi.org/10.4271/2020-01-0136.Data Sets - Support Documents
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