Handling Pedestrians in Crosswalks Using Deep Neural Networks in the IARA Autonomous Car
| Autor: | Ranik Guidolini, Lucas G. Scart, Claudine Badue, Luan F. R. Jesus, Thiago Oliveira-Santos, Vinicius B. Cardoso |
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| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering 02 engineering and technology Pedestrian Pipeline (software) Convolutional neural network Set (abstract data type) Lidar 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | IJCNN |
| Popis: | In this work, we propose a subsystem to handle pedestrians in crosswalks using deep neural networks for the IARA autonomous car, which relies on camera and LIDAR data fusion. Crosswalks’ positions were manually annotated in IARA’s map. Pedestrians are detected in the camera image using a convolutional neural network (CNN). Then, pedestrians’ positions in the map are obtained by fusing their positions in the image with the LIDAR point cloud. Subsequently, if a pedestrian position is inside the crosswalk area, the crosswalk is set as busy. Finally, a busy crosswalk message is published to the High-Level Decision Maker subsystem. This subsystem selects the car’s behavior according to the crosswalk condition and propagates this decision down through the control pipeline, in order to make the car drive correctly through the crosswalk area. The Pedestrian Handler subsystem was evaluated on IARA, which was driven autonomously for various laps along a real and complex circuit with various crosswalks. In all passages through crosswalks, the Pedestrian Handler dealt with pedestrians as expected, i.e., without any human intervention. |
| Databáze: | OpenAIRE |
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