Autonomous Driving › Summary

Autonomous driving puts world models under physical-road constraints that shape the choices a system must make.

In many systems, sensor features are fused into a bird's-eye-view or occupancy representation of the surrounding scene. Observed, inferred, and predicted occupancy should be distinguished when interpreting model outputs, even if a system estimates them jointly; confusing their meanings can make a planner overconfident.

Other-agent behavior may be multimodal. A single average trajectory cannot represent a yield-or-continue choice. Candidate-set coverage, probability calibration, and downstream decisions all matter; no one displacement metric is sufficient.

Closed-loop simulation evaluates policy–environment interaction and complements prediction benchmarks, log replay, and real-world evidence. Reactive-agent and sensor-level setups expose different failure modes, and every simulator has a simulation-to-reality gap.

Rare events, safety, and planning tie everything together. Long-tail coverage, uncertainty calibration, planning under uncertainty, and defenses against model exploitation are important design and evaluation considerations. No single list of methods certifies deployment safety. The planner's job is to select actions that respect the map and rules while accounting for predicted alternatives and possible model error.

Our worked example showed how a single-trajectory MSE predictor regresses toward a mean path that can hide collision risk. A two-mode predictor trained with synthetic mode labels avoids overlaps between the toy vehicle footprints under the same threshold decision rule, but brakes more often than both the oracle and a constant-velocity baseline. The result is an action-outcome illustration, not evidence of real-world driving safety or of unsupervised mode discovery.