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How would you predict a car’s turning intention?

MetaData Scientist · Onsite
MediumData ScientistOnsiteMachine Learning

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This question evaluates applied machine learning skills for vehicle intention prediction, covering temporal sequence modeling versus feature engineering, map- and lane-aware representations, multi-agent interaction reasoning, labeling/time-horizon definition, evaluation metrics and calibration, class imbalance and distribution shift handling, and uncertainty estimation for downstream planning. It is commonly asked because intention prediction is safety-critical in autonomous driving and probes a candidate’s ability to make system-level design trade-offs across data labeling, model architecture, training/validation, and risk-aware evaluation. Category/Domain: Machine Learning; Abstraction level: system-level applied ML for data scientist roles (mid-to-senior).

At an intersection, there are n vehicles stopped or approaching.

For each vehicle, you have a short history (e.g., last 3–10 seconds at 10 Hz) of:

  • Position (x, y) in a map-aligned coordinate system
  • Velocity (vx, vy) and optionally acceleration
  • (Optional) lane/map context (lane centerlines, stop lines)

You want to predict each vehicle’s intention over the next few seconds as one of:

  • Left turn
  • Go straight
  • Right turn

Task: Describe how you would build, train, and evaluate an ML model for intention prediction.

In your answer, cover:

  1. Labeling strategy and time horizon (when is intention defined?).
  2. Feature engineering vs sequence models; how to incorporate map/lane geometry.
  3. How to handle multi-agent interactions (other cars influence intent).
  4. Evaluation metrics (accuracy vs calibration vs early prediction; per-class metrics; cost-sensitive errors).
  5. Handling class imbalance, distribution shift across intersections/cities, and intent changes at the last second.
  6. How you would estimate uncertainty and use it in downstream planning.