Datasets:

InterestingITS
/

U-EASWS

# U-EASWS processing code

The scripts provide coordinate conversion, trajectory reconstruction, vehicle-length estimation, TTC/PET/DRAC annotation, vehicle pairing, balanced sampling, driver-space parameter estimation and interaction annotation. The four measured calibration configurations contain the actual fitting inputs for sixteen source videos. Paths are supplied through command-line arguments.

## Installation

Use Python 3.12 and keep the Python files together. Run commands from this directory:

```bash
python -m venv .venv
# Activate .venv using the command appropriate to your operating system.
python -m pip install -r requirements.txt
```

Every executable script accepts `--help`. Output files must have new names. For commands producing several outputs, all destination paths must be distinct and not already exist.

## Start with the released Basic trajectory files

Download a Basic trajectory CSV from the [U-EASWS repository](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main) and place it under `data/basic/`. The Basic files already contain `Lane number`, `Direction`, metric positions, velocities and estimated vehicle lengths. Use these fields directly for downstream processing. `configs/basic_manifest.csv` gives the four observation groups and the local filenames used in the examples; if the downloaded filename differs, pass its actual path in `--input` or rename it to the listed local filename without changing its records.

The following commands use the existing WS3 Basic file and its recorded lane and direction fields:

```bash
python annotate_risk.py --input "data/basic/Weaving Segment_3.csv" --output runs/ws3_risk.csv
python generate_vehicle_pairs.py --input "data/basic/Weaving Segment_3.csv" --output runs/ws3_pairs.csv --audit runs/ws3_pairs_summary.json
python balance_pair_samples.py --input runs/ws3_pairs.csv --output runs/ws3_balanced.csv --audit runs/ws3_sampling_summary.json --seed 42
python fit_pr_parameters.py --input runs/ws3_balanced.csv --output runs/ws3_pr_parameters.csv --audit runs/ws3_fit_summary.json --workers 1
python annotate_interactions.py --input "data/basic/Weaving Segment_3.csv" --pairs runs/ws3_pairs.csv --parameters runs/ws3_pr_parameters.csv --output runs/ws3_interactions.csv
```

The pair, sample and inference commands operate on the complete observation-group file. Fit parameter groups are defined by `cangle,round_v`. Use the full candidate-pair file for interaction annotation; the balanced subset is for fitting.

Risk annotation uses `Time_s` when present. For the released format containing `Delta_t_s`, it reconstructs a shared time reference for each connected sequence of observed frame intervals. This uses the recorded 30/60 fps intervals, including mixed-rate files. Disconnected sequences receive independent `Time_Reference_ID` values and start at `Time_s=0`; elapsed time between such references is not inserted. Trajectories must remain within one reference. An explicit `--fps` is an alternative only for a recording with that constant frame rate.

## Start with source detection records

The pixel-to-metric and reconstruction commands are separate entry points for source detection records. Use `configs/source_manifest.csv` to match each `Raw_Data` CSV to the actual source FPS and calibration source key.

| Observation group | Master source | Measured configuration |
| --- | --- | --- |
| WS1-2 | video4 | [WS1_2.json](calibrations/WS1_2.json) |
| WS3 | video2 | [WS3.json](calibrations/WS3.json) |
| WS4-5 | video1 | [WS4_5.json](calibrations/WS4_5.json) |
| WS6-7 | video2 | [WS6_7.json](calibrations/WS6_7.json) |

All supplied JSONs in `calibrations/` contain measured scene values. See [CALIBRATION.md](docs/CALIBRATION.md) for the fields and transformation equations. Raw file suffix `_N` corresponds to `videoN`.

For raw reconstruction, supply a source-frame annotation CSV containing `Frame,Vehicle_ID,Lane number,Direction`, keyed by the original detection records, or use metric detections that already contain these fields. The published Basic data provide lane and direction fields under processed `Frame,Ego_Vehicle_ID`. To transfer those fields to raw detections, apply the corresponding source/frame/vehicle mapping to obtain the original keys.

The source-frame example below uses WS3/video2 at 30 fps and `data/lanes/WS3_video2_lanes.csv` as the annotation input:

```bash
python coordinate_transform.py --input data/raw/WS3_raw_detection_data_2.csv --calibration calibrations/WS3.json --source video2 --output runs/ws3_video2_metric.csv
python attach_lane_annotations.py --input runs/ws3_video2_metric.csv --annotations data/lanes/WS3_video2_lanes.csv --output runs/ws3_video2_metric_with_lanes.csv
python reconstruct_trajectories.py --input runs/ws3_video2_metric_with_lanes.csv --source video2 --fps 30 --id-offset 0 --frame-offset 0 --time-offset 0 --output runs/ws3_video2_reconstructed.csv --rejected runs/ws3_video2_rejected.csv --audit runs/ws3_video2_screening.csv
python estimate_vehicle_length.py --input data/raw/WS3_raw_detection_data_2.csv --calibration calibrations/WS3.json --source video2 --output runs/ws3_video2_lengths.csv
python merge_vehicle_lengths.py --input runs/ws3_video2_reconstructed.csv --lengths runs/ws3_video2_lengths.csv --output runs/ws3_video2_basic.csv
```

The generated Basic file can then be supplied to the downstream commands above. If the metric detection input already includes source-frame lanes/direction, omit the lane-join command and pass that file to reconstruction.

Use one source recording per reconstruction. The example offsets retain source-local IDs and time; supply established offsets explicitly when combining sources. `filter_trajectory_outliers.py` exposes the same screen used inside reconstruction. Measured `--merge-x-m` and `--diverge-x-m` reference coordinates enable the corresponding distance fields.

See [INPUT_OUTPUT.md](docs/INPUT_OUTPUT.md) for all input schemas and [METHOD_NOTES.md](docs/METHOD_NOTES.md) for processing rules and numerical settings. Attribution for the reused driver-space code is retained in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).

	# U-EASWS processing code

	The scripts provide coordinate conversion, trajectory reconstruction, vehicle-length estimation, TTC/PET/DRAC annotation, vehicle pairing, balanced sampling, driver-space parameter estimation and interaction annotation. The four measured calibration configurations contain the actual fitting inputs for sixteen source videos. Paths are supplied through command-line arguments.

	## Installation

	Use Python 3.12 and keep the Python files together. Run commands from this directory:

	```bash
	python -m venv .venv
	# Activate .venv using the command appropriate to your operating system.
	python -m pip install -r requirements.txt
	```

	Every executable script accepts `--help`. Output files must have new names. For commands producing several outputs, all destination paths must be distinct and not already exist.

	## Start with the released Basic trajectory files

	Download a Basic trajectory CSV from the [U-EASWS repository](https://huggingface.co/datasets/InterestingITS/U-EASWS/tree/main) and place it under `data/basic/`. The Basic files already contain `Lane number`, `Direction`, metric positions, velocities and estimated vehicle lengths. Use these fields directly for downstream processing. `configs/basic_manifest.csv` gives the four observation groups and the local filenames used in the examples; if the downloaded filename differs, pass its actual path in `--input` or rename it to the listed local filename without changing its records.

	The following commands use the existing WS3 Basic file and its recorded lane and direction fields:

	```bash
	python annotate_risk.py --input "data/basic/Weaving Segment_3.csv" --output runs/ws3_risk.csv
	python generate_vehicle_pairs.py --input "data/basic/Weaving Segment_3.csv" --output runs/ws3_pairs.csv --audit runs/ws3_pairs_summary.json
	python balance_pair_samples.py --input runs/ws3_pairs.csv --output runs/ws3_balanced.csv --audit runs/ws3_sampling_summary.json --seed 42
	python fit_pr_parameters.py --input runs/ws3_balanced.csv --output runs/ws3_pr_parameters.csv --audit runs/ws3_fit_summary.json --workers 1
	python annotate_interactions.py --input "data/basic/Weaving Segment_3.csv" --pairs runs/ws3_pairs.csv --parameters runs/ws3_pr_parameters.csv --output runs/ws3_interactions.csv
	```

	The pair, sample and inference commands operate on the complete observation-group file. Fit parameter groups are defined by `cangle,round_v`. Use the full candidate-pair file for interaction annotation; the balanced subset is for fitting.

	Risk annotation uses `Time_s` when present. For the released format containing `Delta_t_s`, it reconstructs a shared time reference for each connected sequence of observed frame intervals. This uses the recorded 30/60 fps intervals, including mixed-rate files. Disconnected sequences receive independent `Time_Reference_ID` values and start at `Time_s=0`; elapsed time between such references is not inserted. Trajectories must remain within one reference. An explicit `--fps` is an alternative only for a recording with that constant frame rate.

	## Start with source detection records

	The pixel-to-metric and reconstruction commands are separate entry points for source detection records. Use `configs/source_manifest.csv` to match each `Raw_Data` CSV to the actual source FPS and calibration source key.

	\| Observation group \| Master source \| Measured configuration \|
	\| --- \| --- \| --- \|
	\| WS1-2 \| video4 \| [WS1_2.json](calibrations/WS1_2.json) \|
	\| WS3 \| video2 \| [WS3.json](calibrations/WS3.json) \|
	\| WS4-5 \| video1 \| [WS4_5.json](calibrations/WS4_5.json) \|
	\| WS6-7 \| video2 \| [WS6_7.json](calibrations/WS6_7.json) \|

	All supplied JSONs in `calibrations/` contain measured scene values. See [CALIBRATION.md](docs/CALIBRATION.md) for the fields and transformation equations. Raw file suffix `_N` corresponds to `videoN`.

	For raw reconstruction, supply a source-frame annotation CSV containing `Frame,Vehicle_ID,Lane number,Direction`, keyed by the original detection records, or use metric detections that already contain these fields. The published Basic data provide lane and direction fields under processed `Frame,Ego_Vehicle_ID`. To transfer those fields to raw detections, apply the corresponding source/frame/vehicle mapping to obtain the original keys.

	The source-frame example below uses WS3/video2 at 30 fps and `data/lanes/WS3_video2_lanes.csv` as the annotation input:

	```bash
	python coordinate_transform.py --input data/raw/WS3_raw_detection_data_2.csv --calibration calibrations/WS3.json --source video2 --output runs/ws3_video2_metric.csv
	python attach_lane_annotations.py --input runs/ws3_video2_metric.csv --annotations data/lanes/WS3_video2_lanes.csv --output runs/ws3_video2_metric_with_lanes.csv
	python reconstruct_trajectories.py --input runs/ws3_video2_metric_with_lanes.csv --source video2 --fps 30 --id-offset 0 --frame-offset 0 --time-offset 0 --output runs/ws3_video2_reconstructed.csv --rejected runs/ws3_video2_rejected.csv --audit runs/ws3_video2_screening.csv
	python estimate_vehicle_length.py --input data/raw/WS3_raw_detection_data_2.csv --calibration calibrations/WS3.json --source video2 --output runs/ws3_video2_lengths.csv
	python merge_vehicle_lengths.py --input runs/ws3_video2_reconstructed.csv --lengths runs/ws3_video2_lengths.csv --output runs/ws3_video2_basic.csv
	```

	The generated Basic file can then be supplied to the downstream commands above. If the metric detection input already includes source-frame lanes/direction, omit the lane-join command and pass that file to reconstruction.

	Use one source recording per reconstruction. The example offsets retain source-local IDs and time; supply established offsets explicitly when combining sources. `filter_trajectory_outliers.py` exposes the same screen used inside reconstruction. Measured `--merge-x-m` and `--diverge-x-m` reference coordinates enable the corresponding distance fields.

	See [INPUT_OUTPUT.md](docs/INPUT_OUTPUT.md) for all input schemas and [METHOD_NOTES.md](docs/METHOD_NOTES.md) for processing rules and numerical settings. Attribution for the reused driver-space code is retained in [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md).