关键词: 交互场景, 交互数据集, 交互场景描述, 数据生成, 对抗生成, 知识生成, 大语言模型

Abstract:

Interactive traffic scenarios—characterized by high-dimensional complexity and elevated safety stakes—constitute a foremost validation bottleneck for autonomous vehicles (AVs). Recent learning-based scenario-generation paradigms—encompassing data-driven synthesis, adversarial generation, knowledge-guided generation, and large language models (LLMs)—have demonstrated superior fidelity and coverage, substantially improving both the quality of interactive-scenario corpora and the efficiency of safety-critical testing. To systematically chart the advances, core techniques, and remaining impediments in this domain, this paper presents a comprehensive survey of learning-oriented approaches to generating interactive scenarios for AVs, delineating a coherent technical roadmap and future research directions.First, we dissect the fundamental attributes of interactive scenarios and benchmark prevailing open datasets, elucidating their respective capacities to underpin learning-based generators. Concurrently, we review pertinent standards, formal scenario-description languages, and evaluation metrics, analyzing their expressive power over interactive behaviors. Second, we taxonomize existing methods into two strands—conventional learning frameworks and LLM-based paradigms—synthesizing their algorithmic pipelines and empirical generation performance. Finally, we distill cross-cutting challenges residing in data curation and methodological design, and outline prospective research avenues.Our analysis reveals that traditional learning methods struggle to simultaneously guarantee realism and diversity, and remain inefficient in discovering and synthesizing safety-critical edge cases. LLM-based approaches, while exhibiting remarkable proficiency in semantic comprehension and complex logical composition, suffer from sluggish inference, hallucination, misalignment with physical plausibility, and opaque decision-making that undermines interpretability. Moreover, the field at large is constrained by datasets that lack fine-grained interaction-critical annotations and by the absence of unified severity metrics for scenario endangerment. We contend that, despite preliminary successes in interactive modeling, substantial enhancements in generation fidelity and generalizability are imperative to meet the stringent demands of real-world AV deployment.

Key words: interactive scenarios, interactive dataset, interactive scenario description, data generation, adversarial generation, knowledge generation, large language model