Traffic Destination Prediction Eval

---
name: traffic-destination-prediction-eval
description: Evaluates the accuracy of multi-modal trajectory forecasting models in predicting the final destination of traffic agents (pedestrians and vehicles) over a future time horizon. Use when the user wants to benchmark on SDD, InD, Argoverse, or asks about evaluating this task. Reports Minimum final displacement error.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2402.03457
  bibtex_key: [REDACTED]
  confidence: high
---

# traffic-destination-prediction-eval

> Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines — Yousif et al. (2024) (arXiv:2402.03457, 2024)

## What this evaluates

Evaluates the accuracy of multi-modal trajectory forecasting models in predicting the final destination of traffic agents (pedestrians and vehicles) over a future time horizon.

## Datasets

- **SDD** — total ?; splits: test (-1)
- **InD** — total ?; splits: test (-1)
- **Argoverse** — total ?; splits: test (-1)

## Metrics

- `Minimum final displacement error` **(primary)** — range: other
  - For each predicted mode, compute the Euclidean distance between the model's predicted final position and the ground-truth final position. The reported metric is the minimum distance across all predicted modes.

## Input / output format

**Input**: Historical trajectory sequences (positions, acceleration) and contextual features for each agent to be predicted.

**Output**: A set of K predicted future trajectories (one per mode) with associated probabilities, plus the final displacement error computed against ground truth.

## Scoring recipe

```python
def min_fde(predictions, ground_truth, num_modes):
    errors = []
    for k in range(num_modes):
        pred_pos = predictions[k]
        gt_pos = ground_truth
        error = np.sqrt(np.sum((pred_pos - gt_pos) ** 2))
        errors.append(error)
    return min(errors)
```

## Common pitfalls

- Failing to take the minimum over all predicted modes when reporting the error, which unfairly penalizes multi-modal models.
- Mixing up units across datasets (SDD uses pixels, while InD and Argoverse typically use meters), leading to incorrect cross-dataset comparisons.
- Ignoring the multi-modal probability assignment via log-likelihood aggregation, which affects how mode selection is evaluated.

## Evidence (verbatim from paper)

> After training all models, we evaluate their performance using final displacement errors displayed in table 1. The additive models show competitive results on SDD and are comparable to state-of-the-art (SoTA) on InD without road map input. However, they do not perform as well on Argoverse. One factor for that is that cars predication is a harder problem than pedestrians' predication and rely heavily on nearby elements and roadmap. Table 1: Minimum final displacement errors for 20 modes on SDD and InD, and for 6 modes on Argoverse for EBM (ours) and a subset of SoTA previous methods

## Citation

```bibtex
@misc{yousif2024ebmtraffic,
  title={Efficient and Interpretable Traffic Destination Prediction using Explainable Boosting Machines},
  author={Yousif et al. (2024)},
  year={2024},
  note={arXiv:2402.03457}
}
```

- arXiv: 2402.03457