How Data Collection Constraints Shape the Choice Between End-to-End and Modular Autonomous Driving Systems
Question: Explain why data collection often pushes engineers toward a modular autonomous driving system rather than a fully end-to-end one. Compare the kind of training data each approach needs and why one is usually easier to obtain.
Sample answer: A fully end-to-end driving model must be trained on paired examples that connect sensor input, such as forward-facing camera frames, with the driving command the human driver chose, such as steering angle or lane change actions. Building a large dataset of that kind is expensive and slow, because it requires many hours of driving and careful logging of each control decision.
A modular design can break the problem into pieces such as lane detection, vehicle detection, and pedestrian detection. Those subproblems often match data that already exists in public or internal vision datasets, where thousands or millions of labeled images can be gathered much more easily than full driving demonstrations. Because the intermediate labels are more available and cheaper to collect, the modular pipeline is usually the more practical choice.
Key points:
- End-to-end driving systems need paired observations and control actions, which are costly to record at scale.
- Modular systems can use existing labeled datasets for intermediate vision tasks like detecting lanes, cars, and people.
- Easier access to these labels makes a staged pipeline more attractive than a pure end-to-end design.
Rubric: The answer must contrast the high cost of collecting driving-action pairs with the easier availability of labeled intermediate vision data, and explain that this difference makes a modular pipeline more practical.
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How Data Collection Constraints Shape the Choice Between End-to-End and Modular Autonomous Driving Systems
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