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In autonomous driving, the end-to-end (E2E) driving approach that predictsvehicle control signals directly from sensor data is rapidly gaining attention.To learn a safe E2E driving system, one needs an extensive amount of drivingdata and human intervention. Vehicle control data is constructed by many hoursof human driving, and it is challenging to construct large vehicle controldatasets. Often, publicly available driving datasets are collected with limiteddriving scenes, and collecting vehicle control data is only available byvehicle manufacturers. To address these challenges, this paper proposes thefirst self-supervised learning framework, self-supervised imitation learning(SSIL), that can learn E2E driving networks without using driving command data.To construct pseudo steering angle data, proposed SSIL predicts a pseudo targetfrom the vehicle's poses at the current and previous time points that areestimated with light detection and ranging sensors. Our numerical experimentsdemonstrate that the proposed SSIL framework achieves comparable E2E drivingaccuracy with the supervised learning counterpart. In addition, our qualitativeanalyses using a conventional visual explanation tool show that trained NNs byproposed SSIL and the supervision counterpart attend similar objects in makingpredictions.

End-to-End Driving via Self-Supervised Imitation Learning Using Camera and LiDAR Data