This paper proposes a strategy for collecting road frames and steering data for training deep end-to-end control policies for robust lateral vehicle control. During the data collection phase, one camera is deployed in the vehicle and two steering controllers are operated simultaneously. While one controller operates in closed-loop and keeps the vehicle on a desired perturbed trajectory, a second controller computes the nominal steering wheel angle required to drive the vehicle to the center of the desired lane. With this approach, it is possible to train a convolutional neural network to act as an end-to-end lateral vehicle controller that is capable of rejecting unforeseen disturbances. We implement our approach by incorporating the deep learning framework Caffe and the vehicle simulation software TORCS in Matlab/Simulink and analyze the robustness of the trained end-to-end control policy in closed-loop simulations.
- Dynamic Systems and Control Division
Data Collection for Robust End-to-End Lateral Vehicle Control
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Geist, AR, Hansen, A, Solowjow, E, Yang, S, & Kreuzer, E. "Data Collection for Robust End-to-End Lateral Vehicle Control." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 1: Aerospace Applications; Advances in Control Design Methods; Bio Engineering Applications; Advances in Non-Linear Control; Adaptive and Intelligent Systems Control; Advances in Wind Energy Systems; Advances in Robotics; Assistive and Rehabilitation Robotics; Biomedical and Neural Systems Modeling, Diagnostics, and Control; Bio-Mechatronics and Physical Human Robot; Advanced Driver Assistance Systems and Autonomous Vehicles; Automotive Systems. Tysons, Virginia, USA. October 11–13, 2017. V001T45A007. ASME. https://doi.org/10.1115/DSCC2017-5156
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