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In this paper, we propose an end-to-end self-driving network featuring asparse attention module that learns to automatically attend to importantregions of the input. The attention module specifically targets motionplanning, whereas prior literature only applied attention in perception tasks.Learning an attention mask directly targeted for motion planning significantlyimproves the planner safety by performing more focused computation.Furthermore, visualizing the attention improves interpretability of end-to-endself-driving.

Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving