3 papers with code • 3 benchmarks • 3 datasets
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations.
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.
Ranked #1 on Steering Control on Udacity
In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner.