Steering Control
3 papers with code • 3 benchmarks • 3 datasets
Most implemented papers
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks
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.
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
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.
Predicting Model Failure using Saliency Maps in Autonomous Driving Systems
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations.