Pipeline

Features

• Generalised scenario generation from DeepScenario drone data
• Map encoded information
• Comparable/Combinable with existing prediction and planning algorithms
• Comparable with other SOTA algorithms

How to

Scenario Generator

1. Download the DeepUrban ScenarioPreprocessor
   • Construct the file system as suggested in the repository
2. Download the scenario split of the location
   • Splits should be placed int DeepUrban/split to be used for ScenarioCreator and TrajData
3. Choose one of the supported locations datasets
   • This step will redirect you to corresponding Location data from DeepScenario
   • Sign Up or Sign In on the DeepScenario Website
     • Currently supported Versions: Data V1, V2 and Interface V0.8, V0.9)
4. Set source and output folder in DeepUrban/deepurban_scenariocreator/config/default.yaml
5. DeepUrban/deepurban_scenariocreator/src/scenario_preprocessor.py to be used to build Scenarios
6. Scenarios will be saved to DeepUrban/deepurban_scenarios/<location>

TrajData Implementation

1. Download of corresponding lanelet2 map of the location
   • Maps should be placed in DeepUrban/maps to be loaded into TrajData
2. Download the modified TrajData Dataloader
   • An example for the usage of DeepUrban Scenarios has been added deepurban_trajdata/examples/deepurban_example.py
   • Source directory is the DeepUrban/deepurban_scenarios folder as mentioned in Scenario Generator
3. Furhter details can be found in the TrajData Loader DATSETS.md

Supported Locations

Further details