# Motion Prediction Eval

> Evaluates a model's ability to predict future trajectories of pedestrians and other agents in crowded urban environments. It probes how well the architecture captures inter-agent dynamics and interaction patterns over short temporal windows to estimate safe crossing paths. Use when the user wants to benchmark on L-CAS, ETH-Hotel, UCY-Uni, ETH-Univ, Zara01, Zara02, or asks about evaluating this task. Reports Average Displacement Error (ADE).

- Skill: `qhjqhj00/motion-prediction-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/motion-prediction-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/motion-prediction-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/motion-prediction-eval

---


# motion-prediction-eval

> Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing — Radwan et al. (2018) (arXiv:1808.06887, 2018)

## What this evaluates

Evaluates a model's ability to predict future trajectories of pedestrians and other agents in crowded urban environments. It probes how well the architecture captures inter-agent dynamics and interaction patterns over short temporal windows to estimate safe crossing paths.

## Datasets

- **L-CAS** — total ?; splits: test (-1)
- **ETH-Hotel** — total ?; splits: test (-1)
- **UCY-Uni** — total ?; splits: test (-1)
- **ETH-Univ** — total ?; splits: test (-1)
- **Zara01** — total ?; splits: test (-1)
- **Zara02** — total ?; splits: test (-1)

## Metrics

- `Average Displacement Error (ADE)` **(primary)** — range: meters
  - Mean squared error over all predicted and ground-truth points in the trajectory.
- `Final Displacement Error (FDE)` — range: meters
  - Euclidean distance between the predicted and ground-truth poses at the end of the prediction interval.

## Input / output format

**Input**: Sequence of observed agent states (spatial coordinates, velocity, orientation) over an 8-frame (3.2s) sliding observation window.

**Output**: Predicted future agent states (positions, orientations) over a 12-frame (4.8s) prediction horizon.

## Scoring recipe

```python
def compute_ade(pred_traj, gt_traj):
    # pred_traj, gt_traj: (T, 2) arrays of x,y coordinates
    errors = np.sqrt(np.sum((pred_traj - gt_traj)**2, axis=1))
    return np.mean(errors)

def compute_fde(pred_traj, gt_traj):
    return np.sqrt(np.sum((pred_traj[-1] - gt_traj[-1])**2))
```

## Common pitfalls

- Baseline results are copied directly from other papers rather than re-evaluated on the same splits, potentially introducing unfair comparisons due to differing data preprocessing or splits.
- The observation window is implemented as a sliding buffer, allowing predictions immediately after a 5-second initialization phase, not after waiting for the full observation window to elapse.
- Tables often report only translational error (meters), omitting rotational error (degrees) which is also computed and reported in the text.

## Evidence (verbatim from paper)

> We evaluate the accuracy of our motion prediction model by reporting the following metrics: Average Displacement Error: mean squared error over all predicted and groundtruth points in the trajectory. Final Displacement Error: distance between the predicted and groundtruth poses at the end of the prediction interval.

## Citation

```bibtex
@misc{radwan2018multimodal,
  title={Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing},
  author={Radwan et al. (2018)},
  year={2018},
  note={arXiv:1808.06887}
}
```

- arXiv: 1808.06887