Imitation Learning for End to End Vehicle Longitudinal Control with Forward Camera
This addresses autonomous driving speed control for real-world applications, but it is incremental as it builds on existing imitation learning methods.
The paper tackles vehicle speed control using imitation learning from front camera images, achieving correct control in simulation and on a real test track, with promising results on open roads.
In this paper we present a complete study of an end-to-end imitation learning system for speed control of a real car, based on a neural network with a Long Short Term Memory (LSTM). To achieve robustness and generalization from expert demonstrations, we propose data augmentation and label augmentation that are relevant for imitation learning in longitudinal control context. Based on front camera image only, our system is able to correctly control the speed of a car in simulation environment, and in a real car on a challenging test track. The system also shows promising results in open road context.