  Speed-control forecasting, a challenging problem in driver behavior analysis,
aims to predict the future actions of a driver in controlling vehicle speed
such as braking or acceleration. In this paper, we try to address this
challenge solely using egocentric video data, in contrast to the majority of
works in the literature using either third-person view data or extra vehicle
sensor data such as GPS, or both. To this end, we propose a novel graph
convolutional network (GCN) based network, namely, EgoSpeed-Net. We are
motivated by the fact that the position changes of objects over time can
provide us very useful clues for forecasting the speed change in future. We
first model the spatial relations among the objects from each class, frame by
frame, using fully-connected graphs, on top of which GCNs are applied for
feature extraction. Then we utilize a long short-term memory network to fuse
such features per class over time into a vector, concatenate such vectors and
forecast a speed-control action using a multilayer perceptron classifier. We
conduct extensive experiments on the Honda Research Institute Driving Dataset
and demonstrate the superior performance of EgoSpeed-Net.
