Steering Control
7 papers with code • 3 benchmarks • 2 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.
Optimized adaptive MPC for lateral control of autonomous vehicles
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).
PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles
This issue is understudied in both classical and neural-network-based control methods.