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Designing a driving policy for autonomous vehicles is a difficult task.Recent studies suggested an end-toend (E2E) training of a policy to predict caractuators directly from raw sensory inputs. It is appealing due to the ease oflabeled data collection and since handcrafted features are avoided. Explicitdrawbacks such as interpretability, safety enforcement and learning efficiencylimit the practical application of the approach. In this paper, we amend thebasic E2E architecture to address these shortcomings, while retaining the powerof end-to-end learning. A key element in our proposed architecture isformulation of the learning problem as learning of trajectory. We also apply aGaussian mixture model loss to contend with multi-modal data, and adopt afinance risk measure, conditional value at risk, to emphasize rare events. Weanalyze the effect of each concept and present driving performance in a highwayscenario in the TORCS simulator. Video is available in this link:https://www.youtube.com/watch?v=1JYNBZNOe_4

Closing the gap towards end-to-end autonomous vehicle system