Versatile Behavior Diffusion for Generalized Traffic Agent Simulation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911412462288896 |
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| author | Huang, Zhiyu Zhang, Zixu Vaidya, Ameya Chen, Yuxiao Lv, Chen Fisac, Jaime Fernández |
| author_facet | Huang, Zhiyu Zhang, Zixu Vaidya, Ameya Chen, Yuxiao Lv, Chen Fisac, Jaime Fernández |
| contents | Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants, thereby limiting their utility in the evaluation and validation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generation framework based on diffusion generative models that synthesizes scene-consistent, realistic, and controllable multi-agent interactions. VBD achieves strong performance in closed-loop traffic simulation, generating scene-consistent agent behaviors that reflect complex agent interactions. A key capability of VBD is inference-time scenario editing through multi-step refinement, guided by behavior priors and model-based optimization objectives, enabling flexible and controllable behavior generation. Despite being trained on real-world traffic datasets with only normal conditions, we introduce conflict-prior and game-theoretic guidance approaches. These approaches enable the generation of interactive, customizable, or long-tail safety-critical scenarios, which are essential for comprehensive testing and validation of autonomous driving systems. Extensive experiments validate the effectiveness and versatility of VBD and highlight its promise as a foundational tool for advancing traffic simulation and autonomous vehicle development. Project website: https://sites.google.com/view/versatile-behavior-diffusion |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_02524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Versatile Behavior Diffusion for Generalized Traffic Agent Simulation Huang, Zhiyu Zhang, Zixu Vaidya, Ameya Chen, Yuxiao Lv, Chen Fisac, Jaime Fernández Robotics Existing traffic simulation models often fall short in capturing the intricacies of real-world scenarios, particularly the interactive behaviors among multiple traffic participants, thereby limiting their utility in the evaluation and validation of autonomous driving systems. We introduce Versatile Behavior Diffusion (VBD), a novel traffic scenario generation framework based on diffusion generative models that synthesizes scene-consistent, realistic, and controllable multi-agent interactions. VBD achieves strong performance in closed-loop traffic simulation, generating scene-consistent agent behaviors that reflect complex agent interactions. A key capability of VBD is inference-time scenario editing through multi-step refinement, guided by behavior priors and model-based optimization objectives, enabling flexible and controllable behavior generation. Despite being trained on real-world traffic datasets with only normal conditions, we introduce conflict-prior and game-theoretic guidance approaches. These approaches enable the generation of interactive, customizable, or long-tail safety-critical scenarios, which are essential for comprehensive testing and validation of autonomous driving systems. Extensive experiments validate the effectiveness and versatility of VBD and highlight its promise as a foundational tool for advancing traffic simulation and autonomous vehicle development. Project website: https://sites.google.com/view/versatile-behavior-diffusion |
| title | Versatile Behavior Diffusion for Generalized Traffic Agent Simulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.02524 |