LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks

In this work, a deep learning approach has been developed to carry out road detection by fusing LIDAR point clouds and camera images. An unstructured and sparse point cloud is first projected onto the camera image plane and then upsampled to obtain a set of dense 2D images encoding spatial information. Several fully convolutional neural networks (FCNs) are then trained to carry out road detection, either by using data from a single sensor, or by using three fusion strategies: early, late, and the newly proposed cross fusion. Whereas in the former two fusion approaches, the integration of multimodal information is carried out at a predefined depth level, the cross fusion FCN is designed to directly learn from data where to integrate information; this is accomplished by using trainable cross connections between the LIDAR and the camera processing branches. To further highlight the benefits of using a multimodal system for road detection, a data set consisting of visually challenging scenes was extracted from driving sequences of the KITTI raw data set. It was then demonstrated that, as expected, a purely camera-based FCN severely underperforms on this data set. A multimodal system, on the other hand, is still able to provide high accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI road benchmark where it achieved excellent performance, with a MaxF score of 96.03%, ranking it among the top-performing approaches

Deep Learning Applications for Autonomous Driving

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\u27s-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