Localized Safety Datasets

• Localized safety datasets are structured collections defined by precise geo-temporal data and context-specific annotations for safety-critical scenarios.
• They integrate multi-modal sensing and data fusion techniques to support accurate event detection, risk assessment, and decision-making.
• Applications span urban traffic, autonomous systems, and public health, fostering localized interventions and policy innovations.

A localized safety dataset is a structured collection of data designed to support analysis, modeling, and intervention strategies for safety-critical scenarios anchored in a specific spatial, cultural, or contextual domain. These datasets serve as empirical foundations for algorithm development, risk assessment, and decision support in fields including traffic safety, autonomous systems, public health, workplace analytics, and urban design. Localized safety datasets characteristically incorporate spatial precision, multi-modal sensing, event annotation, and context-aware labeling practices to ensure relevance to local hazards and operational norms.

1. Principles and Structural Elements

Localized safety datasets are differentiated from generic safety corpora by their explicit linkage to identifiable locations, cultural or regulatory environments, or context-specific risk factors. Structural elements commonly include:

• Spatial and Temporal Granularity: Datasets often map events or features to precise GPS coordinates, route segments, grid cells, or administrative boundaries, accompanied by time-resolved sampling.
• Multimodal Sensing and Annotation: Inclusion of complementary sensing streams such as stereo and 360° cameras, lidar, radar, IMU, accelerometer, gyroscope, GNSS, and environmental sensors, as in FieldSAFE (1709.03526), AllTheDocks (2404.10528), or US-Accidents (1906.05409).
• Event and Object Labels: Ground truth annotations for events (e.g., accidents, near-misses, cut-ins, hazard encounters) and static or dynamic objects (e.g., obstacles, vehicles, pedestrians). Labels can be in global frames (e.g., bird’s-eye view), local sensor frames, or projected onto maps.
• Contextual Metadata: Weather, lighting condition, road or infrastructure typology, demographic data for raters or participants, detailed operational context (e.g., agricultural machinery in FieldSAFE, cycling infrastructure in AllTheDocks).
• Sociocultural and Demographic Annotation: Demographic breakdown of raters (as in DICES (2306.11247)), community-driven preference structures (LIVS (2503.01894)), or location-specific language variants (RabakBench (2507.05980), Amplify Initiative (2504.14105)).

2. Data Acquisition and Processing Methodologies

The collection and preparation of localized safety datasets employ rigorous multi-stage methodologies suited to the domain:

• Sensor Fusion: Accurate positioning often relies on fusing GNSS and IMU data using Kalman filtering or weighted combinations (FieldSAFE: $$p_t = \alpha p_{\text{GNSS}} + (1-\alpha)[p_{\text{IMU}} + \Delta t \cdot v]$$).
• Event Synchronization and Registration: Complex hardware/software synchronization (FieldSAFE; CitySim (2208.11036)) aligns disparate sensors, sometimes using drone-based orthophotos or manual event markers.
• Ground Truthing: Manual or semi-automated labeling (drone videos in FieldSAFE; cyclist panel rating in AllTheDocks; active learning correction in CitySim) with transformation between reference frames, supported by domain experts as in Amplify Initiative.
• Normalization and Cleaning: Removal of personal identifiers, temporal/spatial normalization, calibration (e.g., ellipsoid fitting for magnetometer data (2411.07315)), imputation of missing data, downsampling or harmonization of sensor rates.
• Synthetic Data Generation: In privacy-sensitive or event-scarce contexts, as with SynSHRP2 (2505.06276) or Urban Anomalies (2410.01844), events are reconstructed or simulated, often using methods such as Stable Diffusion with ControlNet to ensure de-identification and preservation of safety-relevant signals.

3. Domain-Specific Designs and Use Cases

Localized safety datasets address the distinctive characteristics of target domains:

Domain	Sensor/Labeling Approaches	Example Datasets & Features
Agriculture	Multi-modal fusion, GNSS/IMU, drone labeling	FieldSAFE: obstacle types incl. humans, rocks, barrels (1709.03526)
Urban Traffic	Drone, roadside camera, LiDAR, multi-agent	CitySim: rotated bboxes, minTTC/PET events (2208.11036)<br>Accid3nD: multi-sensor 3D, rule+learning accident model (2503.12095)
Cyclist Safety	GoPro + IMU, IRI computation, crowd annotation	AllTheDocks: road roughness, Likert safety ratings (2404.10528)
Human Mobility	GPS, simulated, anomaly injection	Urban Anomalies: hunger, work, social anomalies; SEIR spread (2410.01844)
Language Safety	Local text, adversarial testcases, multilingual	RabakBench: Singlish-Malay-Tamil-Chinese, red-teaming (2507.05980)<br>Amplify: African local expert queries (2504.14105)
Conversational AI	Demographically rich rater panels, fine-grained metadata	DICES: 100+ raters per case, diversity scoring (2306.11247)
VRU Trajectories	Rooftop cameras, LiDAR/radar, signal timing	OnSiteVRU: 17k+ VRU/vehicle tracks, 0.04s precision (2503.23365)
Workplace Safety	Weighted oversampling, severity/frequency/type	EAT framework for incident balancing, multiple open datasets (2408.07094)

Applications span real-time hazard detection (Accid3nD, CitySim), risk modeling and prediction (Pedestrian Patterns (2001.01816)), infrastructure planning (US-Accidents, AllTheDocks), simulation (digital twins in CitySim), or even context-aware T2I model alignment for inclusive public spaces (LIVS (2503.01894)).

4. Benchmarking, Evaluation, and Analysis

Rigorous benchmarking is key to the utility and comparability of localized safety datasets:

• Ground Truth and Error Metrics: Datasets provide ground truth against which models can assess object detection, trajectory prediction, or localization, often using metrics such as mean Average Precision (mAP), Intersection over Union (IOU), minADE/minFDE (OnSiteVRU), Root Mean Square Error (RMSE in LocaRDS (2012.00116)), or coverage rates for localization.
• Aggregation and Aggregation Strategies: Multi-label annotation (e.g., DICES overall rating $Q_{\text{overall}}$ via prioritized sub-task aggregation), majority or plurality voting in multicultural rater settings, or adversarial example selection by model “red teaming” (RabakBench).
• Domain-Sensitive Scenarios: The inclusion of digitally simulated or rare events (SynSHRP2, Urban Anomalies), or imbalanced events (EAT-based datasets), and context-specific harm taxonomies (RabakBench, DICES, Amplify) enables nuanced evaluation of algorithms’ local robustness.
• Novel Metrics: Specialized metrics, such as the spatial-temporal area under the curve (STAUC) in DoTA (2401.03587), or similarity-based matching like the Jaccard index for POI calibration (US-Accidents: $$\text{Jaccard}(S_1, S_2) = \frac{|S_1 \cap S_2|}{|S_1 \cup S_2|}$$).

5. Challenges and Considerations

Localized safety dataset construction is fraught with technical and practical challenges:

• Privacy and Ethics: Personal data, PII, and sensitive location information (SynSHRP2, urban mobility datasets) require de-identification via synthetic recasting or aggregation.
• Rarity and Imbalance: Safety-critical events may be intrinsically rare (near-misses, accidents, rare crimes), leading to severe class imbalance and necessitating domain-aware oversampling strategies (EAT-ROS, EAT-SMOTE, EAT-ADASYN (2408.07094)).
• Cultural and Linguistic Nuance: Multilingual, code-mixed, and regionally nuanced language (RabakBench, Amplify Initiative) present issues for both annotation and model robustness, often exposing sharp degradation in guardrail performance on code-mixed or low-resource languages.
• Annotation Ambiguity: Community or demographic disagreement (DICES, LIVS) indicates ambiguity in what constitutes “safe,” making multi-criteria, intersectional approaches and retention of annotation distributions essential.
• Sensor Calibration and Environmental Variability: Environmental factors—weather, lighting, road surface—impact sensor reliability (CitySim, Accid3nD), necessitating careful calibration and procedural validation.

6. Future Directions and Expansion

Recent literature highlights several forward-looking directions:

• Expanding Modalities and Coverage: Integrating additional sensors (audio, weather, new IoT sources), augmenting with 3D and high-frequency sampling (OnSiteVRU, Accid3nD), or enriching demographic reach (LIVS, DICES).
• Adaptive and Participatory Frameworks: Leveraging participatory design for criteria and concept selection (LIVS), democratizing data creation (Amplify), or using active learning for more targeted labeling (CitySim).
• Synthetic Data and Privacy: Increased use of synthetic, privacy-preserving reconstructions (SynSHRP2) to overcome the accessibility barrier for real-world SCEs while maintaining applicability to local safety research.
• Benchmarking for Localized AI Safety: Structured, reproducible pipelines for generating, translating, and labeling adversarial or nuanced safety data in under-resourced languages and cultural settings (RabakBench).
• Multicriteria, Context-Aware Evaluation: Ongoing research into alignment methods that model and respect the heterogeneity and ambiguity in local safety perceptions (editor’s term: “pluralistic safety alignment”).

7. Impact and Accessibility

Localized safety datasets are a cornerstone for practical safety innovation across domains from smart cities and autonomous vehicles to workplace management and AI moderation systems. Their accessibility frequently determines the inclusivity of safety-focused technological advances, supporting both evidence-based policy intervention and the creation of adaptive, context-responsive AI and automation. Public releases with explicit licensing (as in US-Accidents, OnSiteVRU, RabakBench) represent foundational resources for reproducible research and iterative improvement of localized safety measures.