Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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.  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 porful combination of human and computer intelligence.
['Pranav Rajpurkar', 'Toki Migimatsu', 'Jeff Kiske', 'Royce Cheng-Yue', 'Sameep Tandon', 'Tao Wang', 'Andrew Y. Ng']
I am writing a research paper on a idea Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving
[{"from": "human", "value": "I am writing a research paper on a idea Driverseat: Crowdstrapping Learning Tasks for Autonomous Driving"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.  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.  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 porful combination of human and computer intelligence."}]