用五层结构生成并评估自动驾驶罕见场景，提升仿真真实度。
Driving scenario generation and evaluation using a structured layer representation and foundational models
- 构建五层结构化场景模型，细化每类车的子类与特征。
- 提出多样性与原创性双指标，量化合成数据质量。
- 结合大模型生成逼真场景视频，适合自动驾驶测试。
罕见且具有挑战性的驾驶场景对自动驾驶开发至关重要。由于实际中难以遇到，常通过生成模型进行模拟。在已有分层表示的基础上，本文提出一种五层结构化模型，以改进罕见场景的生成与评估。结合大模型与数据增强策略，生成新场景。该结构为每类交通参与者引入子类与特征，支持基于嵌入的对比分析。研究并适配两种评估指标：多样性分数衡量数据集中场景间的差异性，原创性分数计算合成数据与真实参考集的相似度。论文在多种生成设置下验证了这两个指标，并对基于结构化描述生成的合成视频进行了定性评估。代码与扩展结果见https://github.com/Valgiz/5LMSG。
原文摘要 · Abstract (English)
Rare and challenging driving scenarios are critical for autonomous vehicle development. Since they are difficult to encounter, simulating or generating them using generative models is a popular approach. Following previous efforts to structure driving scenario representations in a layer model, we propose a structured five-layer model to improve the evaluation and generation of rare scenarios. We use this model alongside large foundational models to generate new driving scenarios using a data augmentation strategy. Unlike previous representations, our structure introduces subclasses and characteristics for every agent of the scenario, allowing us to compare them using an embedding specific to our layer-model. We study and adapt two metrics to evaluate the relevance of a synthetic dataset in the context of a structured representation: the diversity score estimates how different the scenarios of a dataset are from one another, while the originality score calculates how similar a synthetic dataset is from a real reference set. This paper showcases both metrics in different generation setup, as well as a qualitative evaluation of synthetic videos generated from structured scenario descriptions. The code and extended results can be found at https://github.com/Valgiz/5LMSG.
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