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Convolutional Neural Networks (CNN) have been successfully applied toautonomous driving tasks, many in an end-to-end manner. Previous end-to-endsteering control methods take an image or an image sequence as the input anddirectly predict the steering angle with CNN. Although single task learning onsteering angles has reported good performances, the steering angle alone is notsufficient for vehicle control. In this work, we propose a multi-task learningframework to predict the steering angle and speed control simultaneously in anend-to-end manner. Since it is nontrivial to predict accurate speed values withonly visual inputs, we first propose a network to predict discrete speedcommands and steering angles with image sequences. Moreover, we propose amulti-modal multi-task network to predict speed values and steering angles bytaking previous feedback speeds and visual recordings as inputs. Experimentsare conducted on the public Udacity dataset and a newly collected SAIC dataset.Results show that the proposed model predicts steering angles and speed valuesaccurately. Furthermore, we improve the failure data synthesis methods to solvethe problem of error accumulation in real road tests.

End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception