  In this paper, we present a novel end-to-end deep neural network model for
autonomous driving that takes monocular image sequence as input, and directly
generates the steering control angle. Firstly, we model the end-to-end driving
problem as a local path planning process. Inspired by the environmental
representation in the classical planning algorithms(i.e. the beam curvature
method), pixel-wise orientations are fed into the network to learn
direction-aware features. Next, to handle the imbalanced distribution of
steering values in training datasets, we propose an improvement on a
cost-sensitive loss function named SteeringLoss2. Besides, we also present a
new end-to-end driving dataset, which provides corresponding LiDAR and image
sequences, as well as standard driving behaviors. Our dataset includes multiple
driving scenarios, such as urban, country, and off-road. Numerous experiments
are conducted on both public available LiVi-Set and our own dataset, and the
results show that the model using our proposed methods can predict steering
angle accurately.
