  While emerging deep-learning systems have outclassed knowledge-based
approaches in many tasks, their application to detection tasks for autonomous
technologies remains an open field for scientific exploration. Broadly, there
are two major developmental bottlenecks: the unavailability of comprehensively
labeled datasets and of expressive evaluation strategies. Approaches for
labeling datasets have relied on intensive hand-engineering, and strategies for
evaluating learning systems have been unable to identify failure-case
scenarios. Human intelligence offers an untapped approach for breaking through
these bottlenecks. This paper introduces Driverseat, a technology for embedding
crowds around learning systems for autonomous driving. Driverseat utilizes
crowd contributions for (a) collecting complex 3D labels and (b) tagging
diverse scenarios for ready evaluation of learning systems. We demonstrate how
Driverseat can crowdstrap a convolutional neural network on the lane-detection
task. More generally, crowdstrapping introduces a valuable paradigm for any
technology that can benefit from leveraging the powerful combination of human
and computer intelligence.
