Image-Based Real-Time Path Generation Using Deep Neural Networks
Gabriel Moraes, Anderson Mozart, Pedro Azevedo, Marcos Piumbini, Vinicius B. Cardoso, Thiago Oliveira-Santos, Alberto Ferreira De Souza, Claudine Santos Badue · 2020
We propose an image-based real-time path planner for the self-driving car IARA, named DeepPath. DeepPath uses a CNN for inferring paths from images. During the self-driving car operation, DeepPath receives an image and the current car pose. Then, it sends the image to a CNN trained to infer a model of the path. After that, DeepPath generates the path in the IARA's coordinate system using the path model. Subsequently, given the current IARA's pose, DeepPath transforms each pose of the path in the IARA's coordinate system into another pose in the world coordinate system. Finally, it sends the path to the IARA's Behavior Selector subsystem, the next subsystem in the IARA's Decision-Making system. We evaluated the performance of DeepPath in real world scenarios. Our results showed that DeepPath is able to correctly generate paths for IARA that differ only slightly from those defined by humans.