RealGen: Retrieval Augmented Generation
for Controllable Traffic Scenarios

Wenhao Ding*1, 2    Yulong Cao*2    Ding Zhao1    Chaowei Xiao2, 3    Marco Pavone2, 4   
1CMU       2NVIDIA       3UW Madison       4Stanford
Published at ECCV 2024 as an Oral paper.

Conventional methods make the model memorize the data distribution for generating. In contrast, our method employs a retriever to query datasets (including external data obtained after training) and uses a generative model to generate scenarios by integrating the information from the retrieved scenarios.

Querying similar and dissimilar scenarios with RealGen

A vehicle (cyan color) goes straight alone on the road.

Query Scenario

Similar Scenario 1

Similar Scenario 2

Dissimilar Scenario

A vehicle (cyan color) yelids other vehicles.

Query Scenario

Similar Scenario 1

Similar Scenario 2

Dissimilar Scenario

Two vehicles drive in opposite directions.

Query Scenario

Similar Scenario 1

Similar Scenario 2

Dissimilar Scenario

Vehicles start to move in an intersection.

Query Scenario

Similar Scenario 1

Similar Scenario 2

Dissimilar Scenario

A vehicle yields another vehicle in an intersection.

Query Scenario

Similar Scenario 1

Similar Scenario 2

Dissimilar Scenario

Generated scenarios retrieved by tags

Yielding

Initial pose and map

Generated scenario

Overtake

Initial pose and map

Generated scenario

U-Turn

Initial pose and map

Generated scenario

Crash scenarios generated by RealGen

Initial pose and map

Generated scenario

Initial pose and map

Generated scenario

Initial pose and map

Generated scenario

Initial pose and map

Generated scenario

Initial pose and map

Generated scenario

Initial pose and map

Generated scenario