Name: RAPHAEL VIVACQUA CARNEIRO
Publication date: 24/09/2024
Examining board:
Name |
Role |
|---|---|
| ALBERTO FERREIRA DE SOUZA | Presidente |
| CLAUDINE SANTOS BADUE | Examinador Interno |
| KARIN SATIE KOMATI | Examinador Externo |
| MARIELLA BERGER ANDRADE | Examinador Externo |
| THOMAS WALTER RAUBER | Examinador Interno |
Summary: This work proposes the use of deep neural networks (DNN) for solving the problem
of inferring the location of drivable lanes of roadways and their relevant properties such
as the lane change right-of-way, even if the line markings are poor or absent. This
problem is relevant to the operation of self-driving cars which requires precise maps
and precise path plans. Our approach to the problem is the use of a DNN for semantic
segmentation of LiDAR remission grid maps into road grid maps. Both LiDAR
remission grid maps and road grid maps are square matrices in which each cell
represents features of a small 2D-squared region of the real world (e.g., 20cm × 20cm).
A LiDAR remission grid map cell contains the information about the average intensity
of laser reflection remission on the surface of that particular place. A road grid map cell
contains the semantic information about whether it belongs to either a drivable lane or
a line marking or a non-drivable area. The semantic codes associated with the road
map cells contain all information required for building a network of valid paths, which
are required for self-driving cars to build their path plans. Our proposal is a novel
technique for the automatic building of viable path plans for self-driving cars. In our
experiments we use the self-driving car of UFES, IARA (Intelligent Autonomous
Robotic Automobile). We built datasets of manually marked road lanes and use them
to train and validate the DNNs used for the semantic segmentation and the generation
of road grid maps from laser remission grid maps. The results achieved an average
segmentation accuracy of 94.7% in cases of interest. The path plans automatically
generated from the inferred road grid maps were tested in the real world using IARA
and has shown performance equivalent to that of manually generated path plans.
