Data Collection for Robust End-to-End Lateral Vehicle Control

Title: Data Collection For Robust End-to-End Lateral Vehicle Control
A. René Geist, Andreas Hansen, Eugen Solowjow, Shun Yang, Edwin Kreuzer
2017 Dynamic Systems and Control Conference
Abstract: This paper proposes a strategy for collecting image 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 con-
trollers are operated simultaneously. While one controller oper-
ates 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 con-
volutional 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.
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