2 research outputs found
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
We propose a new method for fusing a LIDAR point cloud and camera-captured
images in the deep convolutional neural network (CNN). The proposed method
constructs a new layer called non-homogeneous pooling layer to transform
features between bird view map and front view map. The sparse LIDAR point cloud
is used to construct the mapping between the two maps. The pooling layer allows
efficient fusion of the bird view and front view features at any stage of the
network. This is favorable for the 3D-object detection using camera-LIDAR
fusion in autonomous driving scenarios. A corresponding deep CNN is designed
and tested on the KITTI bird view object detection dataset, which produces 3D
bounding boxes from the bird view map. The fusion method shows particular
benefit for detection of pedestrians in the bird view compared to other
fusion-based object detection networks.Comment: 10 pages, 6 figures, 3 table
Joint Attention in Driver-Pedestrian Interaction: from Theory to Practice
Today, one of the major challenges that autonomous vehicles are facing is the
ability to drive in urban environments. Such a task requires communication
between autonomous vehicles and other road users in order to resolve various
traffic ambiguities. The interaction between road users is a form of
negotiation in which the parties involved have to share their attention
regarding a common objective or a goal (e.g. crossing an intersection), and
coordinate their actions in order to accomplish it. In this literature review
we aim to address the interaction problem between pedestrians and drivers (or
vehicles) from joint attention point of view. More specifically, we will
discuss the theoretical background behind joint attention, its application to
traffic interaction and practical approaches to implementing joint attention
for autonomous vehicles