Rapid Autonomous Car Control based on Spatial and Temporal Visual Cues
This addresses autonomous racing for robots, but it appears incremental as it combines existing deep learning methods without clear SOTA claims.
The paper tackles autonomous car control by using a DCNN/LSTM model to predict steering angles and throttle values, enabling a robot to stay on path and avoid collisions during races.
We present a novel approach to modern car control utilizing a combination of Deep Convolutional Neural Networks and Long Short-Term Memory Systems: Both of which are a subsection of Hierarchical Representations Learning, more commonly known as Deep Learning. Using Deep Convolutional Neural Networks and Long Short-Term Memory Systems (DCNN/LSTM), we propose an end-to-end approach to accurately predict steering angles and throttle values. We use this algorithm on our latest robot, El Toro Grande 1 (ETG) which is equipped with a variety of sensors in order to localize itself in its environment. Using previous training data and the data that it collects during circuit and drag races, it predicts throttle and steering angles in order to stay on path and avoid colliding into other robots. This allows ETG to theoretically race on any track with sufficient training data.