scenario-characterization 0.3.9

Automated Scenario Characterization Toolkit.

ScenarioCharacterization

Note: This project is a work in progress.

A generalizable, automated scenario characterization framework for trajectory datasets. This project is primarily a re-implementation of the scenario characterization approach introduced in SafeShift, as part of an internship project at StackAV.

Categorical Scores	Animated Scenarios	Static Scenarios
Result from our categorical profiler. Agents are visualized from dark green (low crit.) to dark red (high crit.) based on their criticality w.r.t. the ego-agent (blue).	Result from our animated visualizer, Shows agents by color vehicle (gray), pedestrian (magenta), cyclist (green) and ego (blue), and the scenario's elapsed time throughout the episode.	Result from our static scenario visualizer. Episode's time is shown by increasing trajectory opacity over time.

Repository: github.com/navarrs/ScenarioCharacterization

This repository currently uses:

• uv as the package manager.
• Hydra for hierarchical configuration management.
• Pydantic for input/output data validation.

Installation

Install the package

uv pip install scenario-characterization

Install the package in editable mode

Clone the repository and install the package in editable mode:

git clone [e-mail]:navarrs/ScenarioCharacterization.git
cd ScenarioCharacterization
uv run pip install -e .

To install with Waymo dependencies (required for running the example), use:

uv run pip install -e ".[waymo]"

If installing with dev, run

uv run pip install -e. ".[dev]"
uv run pre-commit install

Documentation

• Organization: Overview of the Hydra configuration structure.
• Schemas: Guidelines for creating dataset adapters and processors that comply with the required input/output schemas.
• Characterization: Details on supported scenario characterization and visualization tools, and how to use them.
• Analysis: Shows how to run feature and score analyses.
• Example: Step-by-step usage example using the Waymo Open Motion Dataset.

Citing

@INPROCEEDINGS{stoler2024safeshift,
  author={Stoler, Benjamin and Navarro, Ingrid and Jana, Meghdeep and Hwang, Soonmin and Francis, Jonathan and Oh, Jean},
  booktitle={2024 IEEE Intelligent Vehicles Symposium (IV)},
  title={SafeShift: Safety-Informed Distribution Shifts for Robust Trajectory Prediction in Autonomous Driving},
  year={2024},
  volume={},
  number={},
  pages={1179-1186},
  keywords={Meters;Collaboration;Predictive models;Robustness;Iron;Trajectory;Safety},
  doi={10.1109/IV55156.2024.10588828}}

Development

Run uv sync --frozen --all-groups to set up environment. Run pre-commit run --all-files to run all hooks on all files.