continuous-control-1

Steering Control

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks

Chen Change Loy, Zheng Ma, Yuenan Hou, Chunxiao Liu•Tue Nov 06 2018

This paper considerably improves 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.

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Predicting Model Failure using Saliency Maps in Autonomous Driving Systems

Zhangyang Wang, Sina Mohseni, Akshay V. Jagadeesh•Sat May 18 2019

A design to train a student model -- a failure predictor -- to predict the main model's error for input instances based on their saliency map is proposed.

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Pixel Level Segmentation Based Drivable Road Region Detection and Steering Angle Estimation Method for Autonomous Driving on Unstructured Roads

Marya Rasib, M. A. Butt, F. Riaz, Adel Sulaiman, M. Akram•Thu Dec 31 2020

A large-scale dataset for drivable region road detection, comprising of 15,000-pixel level high quality fine annotations and 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.

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PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles

Aws Khalil, Jaerock Kwon•Mon Jul 22 2024

This study introduces the Perception Latency Mitigation Network (PLM-Net), a novel deep learning approach for addressing perception latency in vision-based Autonomous Vehicle (AV) lateral control systems, and results validate the efficacy of PLM-Net across various latency conditions.

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Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

Sokratis J. Anagnostopoulos, Jens Bauer, M. Clare, M. Piggott•Tue Mar 28 2023

The novel ML framework WakeNet is presented, which can reproduce generalised 2D turbine wake velocity fields at hub-height over a wide range of yaw angles, wind speeds and turbulence intensities, with a mean accuracy of 99.8% compared to the solution calculated using the state-of-the-art wind farm modelling software FLORIS.

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