Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving

Autonomous driving has evolved from modular perception stacks to prediction-heavy systems and, more recently, end-to-end planning. Early pipelines focused on object detection, lane detection, tracking, and trajectory forecasting. End-to-end driving models then mapped sensor input directly to control outputs, reducing hand-designed interfaces but often leaving little room for explicit future imagination. In parallel, world models became influential in reinforcement learning and robotics because they support internal simulation. Drive-WM brings that idea into real driving scenes, aiming to generate future multiview videos that are useful not only for prediction but also for safe action selection.

The core problem is to model future driving scenes in a way that is both visually faithful and decision-relevant. This requires handling multiple synchronized camera views, temporal evolution, and maneuver-conditioned branching at the same time. Single-view prediction misses geometry and cross-camera agreement; video generation without planning cannot tell which future is safer; and direct action prediction often lacks an explicit mechanism for comparing alternative outcomes. The difficulty is therefore not just to predict the next frames, but to produce controllable, coherent futures that can be scored and used for trajectory choice.

Drive-WM’s innovations are threefold. First, it frames driving as a world-model problem that can interface with end-to-end planners, turning future simulation into a planning asset rather than a separate task. Second, it uses view factorization to jointly model spatial and temporal structure, so the model can maintain multiview consistency while generating motion. Third, it introduces image-based rewards for comparing candidate futures, enabling maneuver-level branching and selection. Together, these pieces move the system from visual forecasting toward safe planning.

The design is novel because it treats multiview future synthesis as the substrate for planning. In most prior work, generation quality and planning utility are optimized separately; here, the generated futures themselves become the basis for action evaluation. This makes the framework especially relevant for real driving, where the best decision is often determined by anticipating the downstream scene configuration.

• �� Input conditioning: The model takes historical multiview observations from a driving scene and a maneuver condition. The maneuver specifies the intended driving action, allowing the model to generate action-dependent futures.

• �� Joint spatiotemporal modeling: Through view factorization, the model decomposes and recombines shared scene factors across cameras and over time. This helps preserve road layout, object motion, and cross-view alignment.

• �� Multiview future synthesis: Rather than predicting a single next state, Drive-WM generates future video sequences for multiple views, aiming for high fidelity and temporal coherence.

• �� Future branching: Different maneuvers induce different possible futures. The model can therefore produce several candidate trajectories from the same current scene, supporting scenario comparison.

• �� Image-based reward scoring: Candidate futures are evaluated using image-based rewards, which act as a proxy for visual plausibility and planning utility. The best-scoring trajectory is selected as the planned outcome.

• �� System compatibility: The framework is explicitly designed to be compatible with existing end-to-end planning models, so it can serve as an imagination module inside a broader autonomous driving stack.

• �� Output: A controllable set of future multiview videos and a selected trajectory that better supports safe driving decisions.

The paper evaluates Drive-WM on real-world driving datasets, although the provided excerpt does not disclose the dataset names. The experimental focus is on four properties: visual fidelity, temporal consistency, cross-view consistency, and controllability under different maneuvers. The setup also emphasizes planning relevance by using image-based rewards to compare alternative futures. In addition to generation evaluation, the paper frames the method as compatible with end-to-end planning models, which suggests a system-level test rather than a purely offline forecasting benchmark. The excerpt does not report specific metrics or hyperparameters.

The reported results indicate that Drive-WM can generate high-quality, coherent multiview future videos in real driving scenes. Importantly, the futures remain consistent across cameras, which is a stronger requirement than single-view realism and supports the claim that view factorization captures scene structure well. The model also supports maneuver-conditioned branching: different intended actions lead to different predicted futures, and image-based rewards are used to choose the best trajectory. The paper positions this as the first demonstration of safe driving planning with a world model, although the shared text does not provide numerical scores or exact dataset identities.

In the short term, Drive-WM can be used for closed-loop scenario simulation, maneuver testing, and offline risk assessment in autonomous driving R&D. Engineers could compare the consequences of lane changes, braking, or merges before deploying policies on-road. In the longer term, the method could become an internal imagination engine inside autonomous vehicles, helping them evaluate candidate actions before execution. It may also support synthetic data generation, planning audits, and safety-centric model debugging in complex urban traffic.

A key limitation is the absence of public quantitative details in the provided text, which makes direct comparison with baselines difficult. Another concern is the reliance on image-based rewards: visual quality is not always the same as traffic safety, so reward alignment may limit reliability in edge cases. Finally, the excerpt does not discuss extreme weather, rare events, or long-horizon stability, all of which are critical for real deployment. Future work should address these gaps with richer safety signals, broader benchmarks, and explicit failure analysis.