Versatile Behavior Diffusion for Generalized Traffic Simulation

Versatile Behavior Diffusion

The model encompasses a multi-modal behavior predictor and a multi-agent diffusion policy for realistic and controllable simulation. The scenario context tokens are encoded through a query-centric Transformer-based encoder. The predictor generates prior distributions of potential behaviors for individual agents in the scene. These marginal behavior priors or other structured forms can be used to guide the multi-agent diffusion policy to generate desired and interactive scenarios.

arXiv

Code

Spotlight Talk - AVAS @ RSS 2024

Our VBD paper won the best paper award at the Workshop on Autonomous Vehicles Across Scales (AVAS) @ RSS 2024. Here is the recording of our spotlight talk.

Second Place Winner @ Waymo Sim Agents Challenge 2024

Our VBD model won the second-place award at the 2024 WOD Sim Agents Challenge. Here is our technical report.

Generate multi-agent scene-consistent and interactive scenarios with VBD

All (up to 32) agents in the scene are controlled by the VBD model.

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Interaction at stop sign

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Cruise

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Complex intersection

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Interaction with side vehicles

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Busy intersection

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Interaction at narrow road

Closed-loop reactive simulation with VBD

SDC (gray) is controlled by a log-playback planner and other agents (blue) are controlled by the VBD model.

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React to SDC lane changing

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Interact with SDC at narrow road

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Interact with SDC at stop sign

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Overtake SDC

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Interact with SDC at left turn

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Interact with SDC at stop sign

Scenario editing with the VBD model's predicted behavior priors

A human user selects the target agent and its target goal from the VBD model's predicted behavior priors. The model's diffusion policy generates scene-consistent scenarios with that condition.

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Nominal sample

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Prior-guided diffusion

Edit: Agent 5 speeds up to pass first

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Nominal Sample

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Prior-guided diffusion

Edit: Agent 1 goes straight

Generate safety-critical scenarios using VBD

We use two guidance strategies for generating safety-critical scenarios in diffusion policy. In game guidance, the green agent is the Avoider, the purple agent is the Adversarial Pursuer, and the blue agents are the others. In prior guidance, the green agent is the Ego and the purple agent is the Adversarial.

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Nominal Sample

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Game-guided safety-critical sample

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Prior-guided safety-critical sample

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Nominal Sample

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Game-guided safety-critical sample

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Prior-guided safety-critical sample

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Nominal Sample

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Game-guided safety-critical sample 1

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Game-guided safety-critical sample 2

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Nominal Sample

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Game-guided safety-critical sample

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No conflict goals found in behavior priors

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