  The training of many existing end-to-end steering angle prediction models
heavily relies on steering angles as the supervisory signal. Without learning
from much richer contexts, these methods are susceptible to the presence of
sharp road curves, challenging traffic conditions, strong shadows, and severe
lighting changes. In this paper, we considerably improve the accuracy and
robustness of predictions through heterogeneous auxiliary networks feature
mimicking, a new and effective training method that provides us with much
richer contextual signals apart from steering direction. Specifically, we train
our steering angle predictive model by distilling multi-layer knowledge from
multiple heterogeneous auxiliary networks that perform related but different
tasks, e.g., image segmentation or optical flow estimation. As opposed to
multi-task learning, our method does not require expensive annotations of
related tasks on the target set. This is made possible by applying contemporary
off-the-shelf networks on the target set and mimicking their features in
different layers after transformation. The auxiliary networks are discarded
after training without affecting the runtime efficiency of our model. Our
approach achieves a new state-of-the-art on Udacity and Comma.ai, outperforming
the previous best by a large margin of 12.8% and 52.1%, respectively.
Encouraging results are also shown on Berkeley Deep Drive (BDD) dataset.
