World model-based long-tail and scenario-specific generation for autonomous driving

Zhang, Cong; Wei, Bangyang; Liu, Yang; Labi, Samuel · 2026 · Crossref

DOI: 10.26599/jicv.2026.9210080

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This editorial addresses the critical safety challenge in autonomous driving (AD) posed by long-tail scenarios—low-frequency, high-complexity events that cause severe accidents through coupled behaviors and environmental factors. Traditional safety analysis methods, including rule-based, simulation-based, and open-loop prediction approaches, are insufficient for evaluating these risks. Open-loop methods, in particular, fail to account for the feedback loop where an agent’s decisions influence subsequent states and interactions, leading to catastrophic failures in dynamic environments. The paper argues that world models offer a paradigm shift by enabling closed-loop inference, allowing systems to understand how their actions reshape the environment over time. The authors analyze how world models facilitate two distinct generation strategies: imagination-based long-tail generation and scenario-specific generation. Imagination-based generation uses world models to actively synthesize plausible worlds and uncover unknown risk patterns. This includes video-based imagination for visual realism, structure-conditioned models using HD maps and occupancy representations for semantic consistency, and latent dynamics-based models that explore low-probability branches of learned world dynamics. These methods move beyond passive data collection to active world construction, though they require validation through physical constraints and planner-in-the-loop evaluations to ensure feasibility. Scenario-specific generation focuses on controllable world construction for targeted stress testing. By shifting from unconditional sampling to conditional formulations, world models can generate scenes based on explicit intent specifications, such as environmental constraints or abnormal agent behaviors like aggressive lane changes. This approach enables counterfactual reasoning and closed-loop targeting, allowing researchers to iteratively refine scenarios based on system reactions. This transforms scenario generation into a goal-driven process that verifies whether planners fail under specific, predefined risk factors, addressing limitations of open-loop predictions that cannot determine when or if a system will fail. The significance of this work lies in redefining scenario generation from a data-centric, open-loop, and coverage-driven process to a world-centric, closed-loop, and risk-driven one. World models allow for the active creation of safety-risk scenarios rather than passive waiting for rare events. However, the authors identify key challenges for future research, including avoiding physically inconsistent hallucinations, quantifying risk for system-level evaluation, and managing error accumulation during long-term interactions. Addressing these issues is essential for leveraging world models as reliable tools for safety evaluation in autonomous driving.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• situational awareness