Multimodal Spatio-Temporal Information in End-to-End Networks for Automotive Steering Prediction

Abstract

We study the end-to-end steering problem using visual input data from an onboard vehicle camera. An empirical comparison between spatial, spatio-temporal and multimodal models is performed assessing each concept's performance from two points of evaluation. First, how close the model is in predicting and imitating a real-life driver's behavior, second, the smoothness of the predicted steering command. The latter is a newly proposed metric. Building on our results, we propose a new recurrent multimodal model. The suggested model has been tested on a custom dataset recorded by BMW, as well as the public dataset provided by Udacity. Results show that it outperforms previously released scores. Further, a steering correction concept from off-lane driving through the inclusion of correction frames is presented. We show that our suggestion leads to promising results empirically.