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This paper presents a learning from demonstration approach to programmingsafe, autonomous behaviors for uncommon driving scenarios. Simulation is usedto re-create a targeted driving situation, one containing a road-side hazardcreating a significant occlusion in an urban neighborhood, and collect optimaldriving behaviors from 24 users. Paper employs a key-frame based approachcombined with an algorithm to linearly combine models in order to extend thebehavior to novel variations of the target situation. This approach istheoretically agnostic to the kind of LfD framework used for modeling data andour results suggest it generalizes well to variations containing an additionalnumber of hazards occurring in sequence. The linear combination algorithm isinformed by analysis of driving data, which also suggests that decision-makingalgorithms need to consider a trade-off between road-rules and immediaterewards to tackle some complex cases.

Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations