Name: GABRIEL ANDRADE NUNES DE MORAES
Type: MSc dissertation
Publication date: 26/05/2022
Advisor:

Name	Role
CLAUDINE SANTOS BADUE	Advisor *

Examining board:

Name	Role
ALBERTO FERREIRA DE SOUZA	Co advisor *
CLAUDINE SANTOS BADUE	Advisor *
THIAGO OLIVEIRA DOS SANTOS	Internal Examiner *

Summary: We propose an image-based real-time path planner for the self-driving car Intelligent Autono-
mous Robotic Automobile (IARA), named DeepPath. DeepPath uses a convolutional neural

network (CNN) for inferring paths from images. During the self-driving car operation, Deep-
Path 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 trans-
forms 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.

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