Analyze how data collection costs influence autonomous driving pipeline design choices
Question: Compare the data collection challenges and costs of a pure end-to-end autonomous driving system versus a multi-stage pipeline that uses intermediate detectors. Explain how data availability shapes the decision to use one system design over the other.
Sample answer: A pure end-to-end autonomous driving system requires a large dataset of image and steering-direction pairs. This data is time-consuming and expensive to collect because it requires people to drive cars around and record their steering direction, making the system difficult to train. In contrast, intermediate modules like car and pedestrian detectors can use existing computer vision datasets that contain large numbers of labeled cars and pedestrians, which are relatively easy and inexpensive to obtain. Since there is high data availability for these intermediate detectors but scarce/expensive data for end-to-end steering mapping, developers are favored to choose a multi-stage, non-end-to-end pipeline over a pure end-to-end system.
Key points:
- End-to-end systems require (Image, Steering Direction) pairs which are expensive and time-consuming to collect via physical driving.
- Intermediate detectors (car/pedestrian) can leverage abundant, easily obtainable labeled image datasets.
- Abundant data for intermediate modules favors using a multi-stage, non-end-to-end pipeline.
Rubric: The answer must compare the collection costs of image/steering-direction pairs with labeled car/pedestrian images, and explain how this difference favors intermediate modules in a multi-stage pipeline.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Related
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