Deep Learning Applications for Autonomous Driving
Licentiatavhandling, 2018

This thesis investigates the usefulness of deep learning methods for solving two important tasks in the field of driving automation: (i) Road detection, and (ii) driving path generation. Road detection was approached using two strategies: The first one considered a bird's-eye view of the driving scene obtained from LIDAR data, whereas the second carried out camera-LIDAR fusion in the camera perspective. In both cases, road detection was performed using fully convolutional neural networks (FCNs). These two approaches were evaluated on the KITTI road benchmark and achieved state-of-the-art performance, with MaxF scores of 94.07% and 96.03%, respectively.

Driving path generation was accomplished with an FCN that integrated LIDAR top-views with GPS-IMU data and driving directions. This system was designed to simultaneously carry out perception and planning using as training data real driving sequences that were annotated automatically. By testing several combinations of input data, it was shown that the FCN having access to all the available sensors and the driving directions obtained the best overall accuracy with a MaxF score of 88.13%, about 4.7 percentage points better than the FCN that could use only LIDAR data.

sensor fusion

computer vision

deep learning

autonomous driving

robotic perception and planning

Lecture hall GD, Chalmers Tvärgata 5, Chalmers
Opponent: Prof. Jim Tørresen, Department of Informatics, University of Oslo, Norway

Författare

Luca Caltagirone

Chalmers, Mekanik och maritima vetenskaper, Fordonsteknik och autonoma system

Fast LIDAR-based road detection using fully convolutional neural networks

IEEE Intelligent Vehicles Symposium, Proceedings,; (2017)p. 1019-1024

Paper i proceeding

LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks

IEEE 20th International Conference on Intelligent Transportation Systems,; (2017)

Paper i proceeding

Caltagirone, L., Bellone, M., and Wahde, M., LIDAR-Camera Fusion for Road Detection using Fully Convolutional Neural Networks

Styrkeområden

Transport

Ämneskategorier

Datorseende och robotik (autonoma system)

Utgivare

Chalmers tekniska högskola

Lecture hall GD, Chalmers Tvärgata 5, Chalmers

Opponent: Prof. Jim Tørresen, Department of Informatics, University of Oslo, Norway

Mer information

Senast uppdaterat

2018-02-01