6 papers with code • 3 benchmarks • 2 datasets
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.
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.
While machine learning systems show high success rate in many complex tasks, research shows they can also fail in very unexpected situations.
This paper proposes an adaptive MPC controller (AMPC) for the path-tracking task and an improved PSO algorithm for optimizing the AMPC parameters.
Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads
Alongside dataset, we also present an end-to-end drivable road region detection and steering angle estimation method to ensure the autonomous driving on generalized urban, rural, and unstructured road conditions.
Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models
We also demonstrate that when utilising the Curl model, WakeNet is able to provide similar power gains to FLORIS, two orders of magnitude faster (e. g. 10 minutes vs 36 hours per optimisation case).