| DOI | Resolve DOI: https://doi.org/10.4271/2020-01-0136 |
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| Author | Search for: Hamieh, Ismail1; Search for: Myers, Ryan1; Search for: Nimri, Hisham1; Search for: Rahman, Taufiq1; Search for: Younan, Aarron; Search for: Sato, Brad; Search for: El-Kadri, Abdul; Search for: Nissan, Selwan; Search for: Tepe, Kemal |
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| Affiliation | - National Research Council of Canada. Automotive and Surface Transportation
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| Format | Text, Article |
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| Conference | WCX SAE World Congress Experience, APR. 21-23, 2020, Detroit Michigan [held virtually due to the COVID-19 pandemic] |
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| Subject | data acquisition and handling; neural networks; autonomous vehicles; machine learning; computer software and hardware; Lidar; trucks; imaging and visualization |
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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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| Publication date | 2020-04-14 |
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| Publisher | SAE International |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 4be1e31f-5495-4773-a3e2-0bb17ef58f26 |
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| Record created | 2020-07-02 |
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| Record modified | 2020-07-23 |
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