Trajectory Prediction & Planning Benchmarks

• Trajectory prediction and planning benchmarks are standardized evaluation protocols that assess model safety, accuracy, and robustness using real-world datasets and multimodal metrics.
• They incorporate modular pipelines featuring dataset adapters, perturbation modules, and unified model interfaces to ensure reproducible and fair comparisons.
• These benchmarks enable end-to-end evaluations using geometric, semantic, and functional metrics, supporting reliable performance in dynamic and adversarial conditions.

Trajectory prediction and planning benchmarks are rigorous, standardized evaluation protocols and datasets designed to assess and compare the accuracy, safety, robustness, and practicality of models that forecast the future motion of agents (vehicles, pedestrians, robots) in dynamic environments. These benchmarks are a critical infrastructure for the development and deployment of autonomous agents, enabling reproducible experiments, fair comparisons, and in-depth analysis of both prediction and planning modules under real-world and adversarial conditions. Recent years have seen the emergence of benchmarks that move beyond isolated, static evaluation, instead integrating metrics for multimodal coverage, safety, scene consistency, diversity, and robustness, while supporting a broad range of use cases from autonomous driving and robotics to human motion analysis.

1. Architectural Foundations of Trajectory Prediction Benchmarks

Modern trajectory benchmarks are structured as modular pipelines, incorporating dataset adapters, preprocessing, scenario extraction, unified model interfaces, standardized metrics, and flexible experiment orchestration. For example, STEP (Structured Training and Evaluation Platform) introduces four layers: dataset adapters (for ingestion and normalization), perturbation/splitting modules (to simulate distribution shifts and define partitions), model interfaces (for consistent training and sampling), and evaluation metrics (Schumann et al., 18 Sep 2025). Atlas similarly organizes its framework into data import, preprocessing, scenario extraction, prediction modules, and evaluation/visualization layers (Rudenko et al., 2022). These systems support fully configurable YAML-driven experiment setups, enable batch-level reproducibility through fixed random seeds, and provide hooks to register user-defined datasets, models, perturbations, and metrics.

A representative schema (from STEP) is:

Module	Function	Example Implementation
Dataset Adapter	Convert raw data to internal schema (X: agent histories, Y: futures)	step/datasets/
Model Interface	Standardize training, sampling (multi-modal, joint predictors)	BaseModel.predict(X, K), train(...)
Perturbation	Simulate noise/distribution shifts/adversarial attacks	step/perturbations/
Metrics	Compute error/collision/diversity rates	step/metrics/

This architectural modularity is fundamental for maintaining comparability across methods and rapidly integrating new evaluation criteria.

2. Dataset Design, Scenario Construction, and Map Integration

Benchmarks are anchored in large-scale, richly annotated datasets reflecting the intended deployment domain. Datasets such as DeepUrban (Selzer et al., 15 Jan 2026), JRDB-Traj (Saadatnejad et al., 2023), Argoverse2, and nuScenes encompass urban driving, crowd navigation, and multimodal traffic in diverse geographies and agent compositions.

• DeepUrban assembles 5,604 dense-traffic scenarios (208,300 agent trajectories, 97% VRUs at Munich Tal site) captured by aerial drones, with global 3D map alignment (OpenDRIVE/lanelet2/VectorMap) enabling complex interaction modeling and multi-agent planning (Selzer et al., 15 Jan 2026).
• JRDB-Traj collects ≈20,000 pedestrian tracks from stereo RGB and 3D LiDAR in both indoor and outdoor settings, employing robot-centered coordinates and cross-modal detection+tracking pipelines (Saadatnejad et al., 2023).
• Scenario extraction mechanisms enforce that agents are sufficiently observed in the past and are mobile, filter out static agents, and support both agent-centric and scene-centric sample definitions.

Map semantics—lane geometries, crosswalks, signals—are accessible and directly incorporated into the scene context passed to predictors and planners, establishing a common environment reference frame for joint forecasting and planning.

3. Evaluation Metrics: Geometric, Semantic, and Functional

Benchmarks operationalize model assessment through precise, task-relevant metrics. The canonical geometric metrics are Average Displacement Error (ADE) and Final Displacement Error (FDE):

$$\mathrm{ADE} = \frac1{NT} \sum_{i=1}^N \sum_{t=1}^T \lVert \hat p_{i,t} - p_{i,t}\rVert_2, \quad \mathrm{FDE} = \frac1N \sum_{i=1}^N \lVert \hat p_{i,T} - p_{i,T}\rVert_2$$

for $N$ agents and horizon $T$. Multimodal metrics (Best-of-K ADE/FDE) select the best-matching predicted mode among $K$ samples.

To capture planning validity and robustness, benchmarks incorporate safety and diversity criteria:

• Offroad Loss and Direction Consistency quantify adherence to drivable areas and traffic-aligned motion (e.g. (Rahimi et al., 2024)):

$$L_{offroad} = \frac{1}{M}\sum_{i=1}^M\sum_{t=1}^T \max(\phi(\bm y^i_t, \Omega)+m, 0)$$

with $\phi(\cdot, \Omega)$ the signed distance to road boundaries.

• Diversity Loss sums pairwise L2 distances among feasible (on-road) modes.
• Collision Rate and Scene Consistency penalize predicted inter-agent collisions:

$$\text{CollRate} = \frac{1}{N}\sum_{i=1}^N \mathbb{1}\{\min_{a\neq b, t}\|\hat x^a_t - \hat x^b_t\|\,<\,d_{min}\}$$

• End-to-end Forecasting Error (EFE) (JRDB-Traj) factors in identity ambiguity and cardinality errors under detection/tracking uncertainty:

$$\text{EFE}(X,Y) = \frac{1}{n} \Biggl[\min_{\pi\in\Pi_n}\sum_{i=1}^m\widetilde{d}(X_i, Y_{\pi(i)}) + c(n-m)\Biggr]$$

Probabilistic metrics (Negative Log-Likelihood, Top-K NLL) evaluate density estimation and coverage. For planning, additional measures—safety margin, time-to-collision, comfort and jerk, and efficiency—directly quantify plan utility and risk (Schumann et al., 18 Sep 2025).

4. Joint Multi-Metric Training and Evaluation Paradigms

Recent benchmarks emphasize integrating multiple loss functions as both training objectives and evaluation metrics, promoting not only accuracy but also safety, compliance, diversity, and real-world viability (Rahimi et al., 2024). The "winner-takes-all" (WTA) regime, where only the best-matching mode is supervised, is supplanted by multi-loss strategies:

$$\mathcal{L}_{final} = \mathcal{L}_{original} + \alpha_1L_{offroad} + \alpha_2L_{dir} + \alpha_3L_{div}$$

By enforcing these terms on all predicted modes, improved on-road compliance (Offroad: –59% in Argoverse 2), better traffic alignment, and increased diversity (+12%) are achieved, at negligible task accuracy loss (Rahimi et al., 2024). Scene-consistent predictors like ScePT employ collision penalties and discrete latent mode sampling to produce fully joint, collision-free forecasts (Chen et al., 2022). This approach aligns evaluation with downstream planning needs, establishing new practical benchmarks for safety and robustness beyond point-wise error.

5. Benchmark Protocols, Robustness, and Extensions to Planning

To systematize evaluation and ensure reproducibility, benchmarks delineate explicit protocols:

• Data splits (random, cross-validation, leave-one-location-out, safety-critical partitioning)
• Hyperparameter optimization with automated tuning pipelines (SMAC3 in Atlas)
• Metric aggregation (mean ± std across seeds, scenario attributes, or perturbations)
• Inclusion of noise injection, adversarial perturbations, and distribution shifts as robustness tests (STEP injects both naturalistic and targeted adversarial perturbations; ADAPT model demonstrated 44% smaller error increase than baseline under attack) (Schumann et al., 18 Sep 2025)

Planning benchmark extensions are realized by interleaving trajectory forecasting modules with planners (e.g., Model Predictive Control) in the evaluation pipeline. Metrics such as time-to-collision and plan comfort integrate prediction uncertainty with control performance. The modularity of frameworks like STEP and Atlas permits simple extension: new planners, scenario samplers, and planning metrics are registered alongside predictors, facilitating end-to-end benchmarking (Schumann et al., 18 Sep 2025, Rudenko et al., 2022).

6. Representative Results and Model Comparisons

Empirical studies across benchmarks demonstrate several salient patterns:

• Adding dense-interaction data (DeepUrban to nuScenes) yields up to 44% improvements in ADE/FDE and ~50% collision reduction (Selzer et al., 15 Jan 2026).
• Scene-level predictors (ADAPT, FJMP, ScePT) reduce collision rates and improve consistency, though sometimes with marginal ADE increases.
• Physics-based human motion models (Social Force, Predictive Social Force) remain competitive with deep learning models in straight trajectory regimes, with superior runtime robustness to noise (Rudenko et al., 2022).
• Adversarial evaluation reveals significant vulnerability of SOTA models (prediction ADE increases of 200+% under attack in rounD) (Schumann et al., 18 Sep 2025).
• Multi-loss strategies yield statistically significant reductions in offroad and direction errors with strong diversity improvements (Rahimi et al., 2024).

Sample results table (from STEP):

Model	ADE6 (Argo2)	FDE6 (Argo2)	CollRate (DJI)
Wayformer	0.65	1.38	0.7%
Trajectron++	0.73	1.65	—
ADAPT	1.01	2.48	0.9%

(Here ADE6/FDE6 is average error over 6 predicted modes. CollRate is percentage of predicted colliding agent pairs.)

7. Future Directions, Best Practices, and Emerging Trends

Concurrent research advocates best practices to strengthen the scientific rigor of benchmarks:

• Use fixed seeds and publish code, configuration, weights, and dataset metadata for full reproducibility.
• Report multi-seed statistics, cross-validation scores, and conduct nuanced sensitivity analysis (horizon length, agent count, data fraction).
• Embrace behavioral and planning-aware tests (collision, scene consistency) alongside standard error metrics.
• Provide hidden test sets and online evaluation servers to curtail overfitting (Selzer et al., 15 Jan 2026).
• Expand evaluation to scenario diversity (corner cases, rare interactions), mixed initiative (human-AV interactions), online adaptation, and explainability (Schumann et al., 18 Sep 2025).

The field is converging on benchmarks that holistically assess prediction and planning in the wild—capturing not just geometric fidelity but also interaction reasoning, robustness, safety, and generalization—serving as a critical enabler for the next generation of autonomous systems.