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.
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
Yielding
Initial pose and map
Generated scenario
Overtake
Initial pose and map
Generated scenario
U-Turn
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
Initial pose and map
Generated scenario