Ego Behavior Prediction Model System Design

**Common mistakes:** Treating this as a generic ML system design without grounding in autonomous driving specifics (ego vs. agent distinction, map context); Outputting only a single deterministic trajectory instead of a multimodal distribution; Not addressing uncertainty quantification at all, or conflating aleatoric and epistemic uncertainty; Ignoring the downstream data contract — failing to specify what the consuming module (planner) actually needs **What passers do:** Candidate with strong domain knowledge (AV-related PhD/internship) who could address inputs, outputs, uncertainty, and downstream contracts fluently; Structured the answer around a production ML pipeline with clear module boundaries; Proactively discussed multimodal trajectory outputs and calibration of uncertainty estimates **Edge cases probed:** How to handle highly ambiguous scenarios (e.g., unprotected left turns, merges) in the output distribution; How downstream consumers should handle low-confidence or high-uncertainty predictions; What happens when ego is in an out-of-distribution environment (e.g., construction zones) **Alternative approaches:** Single deterministic trajectory output (Simpler interface for downstream consumers but loses multimodal uncertainty; fails in ambiguous scenarios like unprotected turns or merges; not suitable for safety-critical planning.); Occupancy grid / probabilistic map output (More general representation than explicit trajectories; easier to aggregate multiple agents; but loses interpretability of discrete intent modes and harder to use directly in trajectory-based planners.); Conditional imitation learning / policy-based prediction (Can capture complex driving styles; but training distribution mismatch (covariate shift) and harder to quantify uncertainty explicitly.)