End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
This work addresses the limitation of single-task steering control in self-driving cars by enabling more comprehensive vehicle control, though it is incremental as it builds on existing end-to-end CNN methods.
The paper tackles the problem of autonomous vehicle control by developing a multi-task learning framework that simultaneously predicts steering angles and speed commands using visual inputs and previous speed feedback, achieving accurate predictions on both the Udacity and a new SAIC dataset.
Convolutional Neural Networks (CNN) have been successfully applied to autonomous driving tasks, many in an end-to-end manner. Previous end-to-end steering control methods take an image or an image sequence as the input and directly predict the steering angle with CNN. Although single task learning on steering angles has reported good performances, the steering angle alone is not sufficient for vehicle control. In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner. Since it is nontrivial to predict accurate speed values with only visual inputs, we first propose a network to predict discrete speed commands and steering angles with image sequences. Moreover, we propose a multi-modal multi-task network to predict speed values and steering angles by taking previous feedback speeds and visual recordings as inputs. Experiments are conducted on the public Udacity dataset and a newly collected SAIC dataset. Results show that the proposed model predicts steering angles and speed values accurately. Furthermore, we improve the failure data synthesis methods to solve the problem of error accumulation in real road tests.