通过建模驾驶者利己与利他特质，生成多样真实交通场景。
SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions
- 分层框架融合语义推理与社会偏好，控制驾驶行为多样性。
- 在Argoverse 2上生成从合作到对抗的多样化交互行为。
- 适合自动驾驶策略训练与高风险场景泛化测试。
仿真中生成真实且多样的交通场景对自动驾驶系统开发与评估至关重要。现有仿真框架多依赖规则或简化模型，缺乏真实世界的保真度与多样性。尽管生成模型已提升交通交互的真实性与上下文感知能力，却常忽略社会偏好对驾驶行为的影响。SocialDriveGen提出一种分层框架，将语义推理、社会偏好建模与轨迹生成相结合。通过将利己与利他视为互补的社会维度，该框架可控制驾驶员个性与互动风格的多样性。在Argoverse 2数据集上的实验表明，SocialDriveGen能生成涵盖合作到对抗行为的高保真场景，显著提升策略在罕见或高风险情境下的鲁棒性与泛化能力。
原文摘要 · Abstract (English)
The generation of realistic and diverse traffic scenarios in simulation is essential for developing and evaluating autonomous driving systems. However, most simulation frameworks rely on rule-based or simplified models for scene generation, which lack the fidelity and diversity needed to represent real-world driving. While recent advances in generative modeling produce more realistic and context-aware traffic interactions, they often overlook how social preferences influence driving behavior. SocialDriveGen addresses this gap through a hierarchical framework that integrates semantic reasoning and social preference modeling with generative trajectory synthesis. By modeling egoism and altruism as complementary social dimensions, our framework enables controllable diversity in driver personalities and interaction styles. Experiments on the Argoverse 2 dataset show that SocialDriveGen generates diverse, high-fidelity traffic scenarios spanning cooperative to adversarial behaviors, significantly enhancing policy robustness and generalization to rare or high-risk situations.
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