PCIWM: A Unified Driving World State for Prediction and Planning

We introduce PCIWM, a driving world model that explicitly constructs physical (P), causal-semantic (C), interaction (I), and ego (E) states and maps them jointly into one world-state representation. A geometry-aware composer, shared transition under external ego-motion inputs, and typed decoders turn this definition into candidate-specific predicted futures. We evaluate the architecture through two connected questions: what it predicts, and what decisions those predictions enable. Controlled nuScenes studies test factor inclusion, composition, relation identity, and transition sharing. Historical controls show relation-prediction and long-horizon stability gains; common-budget and three-seed comparisons reveal observable-dependent tradeoffs and remaining single-step prediction errors. On NAVSIM, predicted traffic improves a component readout over static traffic, and a state-motivated candidate library improves coverage and selected utility over equal-count alternatives. Raw consequences yield a positive but uncertain five-seed selection gain. A further control tests the incremental use of later raw-state updates: repeating each candidate's first predicted state outperforms trajectory-only inputs in an exploratory three-seed comparison, while the full multi-step sequence performs worse than that repetition. These cached, annotation-assisted development studies connect an explicit unified-state architecture to controlled prediction-to-decision evidence, rather than equating predictive fidelity with planning utility.