Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
This work addresses safety in autonomous driving for specific uncommon scenarios, but it is incremental as it builds on existing learning from demonstration methods.
The paper tackles the problem of programming safe autonomous driving behaviors for uncommon hazardous situations by learning from demonstrations, achieving good generalization to variations with multiple sequential hazards.
This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios. Simulation is used to re-create a targeted driving situation, one containing a road-side hazard creating a significant occlusion in an urban neighborhood, and collect optimal driving behaviors from 24 users. Paper employs a key-frame based approach combined with an algorithm to linearly combine models in order to extend the behavior to novel variations of the target situation. This approach is theoretically agnostic to the kind of LfD framework used for modeling data and our results suggest it generalizes well to variations containing an additional number of hazards occurring in sequence. The linear combination algorithm is informed by analysis of driving data, which also suggests that decision-making algorithms need to consider a trade-off between road-rules and immediate rewards to tackle some complex cases.