SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions

TL;DR

SocialDriveGen is a hierarchical generative framework that creates diverse, realistic traffic scenarios by modeling social preferences and interactions, improving autonomous driving system testing.

Contribution

It introduces a novel hierarchical approach integrating social preference modeling with trajectory synthesis for controllable diversity in traffic scenarios.

Findings

Generates diverse, high-fidelity traffic scenarios from cooperative to adversarial behaviors.

Enhances policy robustness and generalization to rare or high-risk situations.

Significantly improves simulation realism over rule-based models.

Abstract

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.