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Read MoreScenario Services for Physical AI to Test the Real World, Safely
Use scenario-based AI services to stress-test Physical AI systems, uncover edge cases, and ship safer, higher-performing products, faster.
What We Deliver
Digital Trust and Safety Services
We start by extracting meaningful scenarios from your real-world logs and test runs.
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- ODD-aware filtering: Focus by geography, road type, weather, traffic patterns, or use-case.
- Critical event detection: Collisions, near-misses, erratic actors, and non-compliant behaviors.
- Taxonomy & tagging: Classify scenarios into nominal (everyday) and edge-case (rare, high-risk) sets aligned with your safety case and requirements.
You get a searchable, versioned library of scenarios that reflects the real conditions your system must handle.
Synthetic Scenario Generation
We expand your coverage by building parameterized synthetic and hybrid scenarios.
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- Log-to-sim reconstruction: Turn real-world logs into replayable simulation scenarios.
- Parameterized variations: Adjust actors, speeds, occlusions, lighting, and weather to cover “what-if” cases.
- Multi-sensor support: Camera, LiDAR, radar, IMU, and multi-sensor fusion workflows.
Scenario Curation & Continuous Refinement
Scenario libraries are living assets, not one-off artifacts. We continuously.
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- De-duplicate & prioritize: Remove noisy or redundant scenarios and highlight those with the highest safety/performance impact.
- Track changes over time: Version scenarios alongside model versions, maps, and ODD shifts.
- Close the loop: Feed new incidents, field data, and customer feedback back into the library.
Result: a high-signal scenario corpus that evolves with your product.
Collision & Near-Collision Analysis
Safety-critical scenarios are analyzed in depth to support your safety case.
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- Event detection at scale: Identify collisions, near misses, and comfort-critical events in large log sets.
- Scenario reconstruction: Rebuild incidents using physics and motion dynamics to understand root causes.
- CAPA support: Provide inputs for Corrective and Preventive Actions, hazard analysis, and test plan updates.
Edge Case Identification & Stress Testing
We help you systematically find and stress-test the edges of your system.
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- Rare events: Unprotected turns, occluded pedestrians, vulnerable road users, intervention-heavy segments.
- Boundary conditions: Identify where performance degrades across speed, visibility, or traffic density ranges.
- Test design: Build targeted scenario sets for model benchmarking, A/B testing, and regression testing.
Product Safety & Comfort Analysis
For consumer-facing autonomy and ADAS products, safety and comfort must move together.
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- Comfort metrics: Analyze jerk, braking, lane position, and interaction behavior vs human benchmarks.
- Ride quality scenarios: Construct “day-in-the-life” scenario sequences to evaluate user experience.
- Comparative studies: Compare model versions or driving policies over the same scenario bundles.
Your teams get clear, scenario-level evidence on how updates impact both safety and passenger comfort.
Accelerate Simulation, Validation, and Deployment with DDD’s Scenario Services
Simulation Ops Services
We create structured test suites covering normal, rare, and high-risk behaviors to ensure model readiness before deployment. Our teams support scenario scaling, test orchestration, defect tracking, and continuous scenario enrichment.
Digital Twin Validation Services
DDD validates dynamic agents, sensor fidelity, environmental attributes, and system performance against real-world benchmarks. This process ensures your digital twins are accurate, interpretable, and optimized for model training, safety validation, and fast iteration cycles.
ODD Analysis Services
We identify environmental, behavioral, infrastructural, and regulatory factors that influence autonomy performance and convert them into targeted scenario coverage. DDD ensures your models are trained and tested within well-defined, continuously improving ODD boundaries.
Edge Case Curation Services
Our workforce analyzes anomalies, rare behaviors, environmental deviations, sensor failures, and unpredictable agent interactions to identify and address these issues. These curated edge cases power safer model retraining, robust scenario generation, and improved generalization.
Industries We Support
Autonomous Driving
We provide large-scale scenario mining, digital twin validation, and edge-case-rich simulations to accelerate safer, faster deployment of full-stack autonomy.
ADAS
DDD delivers simulation-ready scenarios, ODD-focused coverage, and curated edge cases that strengthen perception, fusion, and decision-making safety validation.
Robotics
DDD builds real-world and synthetic scenarios for navigation, manipulation, and human–robot interaction, enabling robust ODD analysis and safer robot behavior.
Healthcare Automation
We support medical robotics with controlled digital twin validation and scenario modeling that stress-test precision, safety, and compliance in critical workflows.
AgTech
DDD creates field-ready scenarios reflecting soil, crop, terrain, and weather variability, enabling edge case curation and ODD analysis for autonomous farm machines.
Humanoids
We generate interaction-heavy, edge-case-rich scenarios and environmental variations so humanoid systems can safely adapt to complex human and workspace conditions.
Why Choose DDD?
Human-in-the-Loop + Scalable Operations
We combine scenario-savvy subject matter experts with trained operations teams to mine and curate scenarios at scale, 24/7, across multiple time zones.
Tool-Chain Agnostic
We integrate into your existing simulation ecosystem, commercial tools, in-house sims, and data platforms, without forcing a platform switch.
Safety & Compliance Mindset
Our workflows are designed to support standards like ISO 26262, ISO 21448 (SOTIF), and NCAP-aligned test strategies by linking scenarios to requirements and test evidence.
Proven Across Autonomy & Defense
From AVs and ADAS to defense tech and robotics, DDD delivers scenario-based solutions that improve system reliability and speed up time-to-deployment.
How We Engage
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Read MoreReady to Operationalize Scenario-based Testing?
Frequently Asked Questions
What are Scenario Services in the context of physical AI?
Scenario Services involve generating, validating, and curating real and synthetic environments that test AI models for safety, robustness, and performance across physical-AI systems.
What kinds of data do you work with?
Camera, LiDAR, radar, IMU, GPS, CAN bus, and derived features across on-road, off-road, and indoor environments.
Can you work with our in-house simulator?
Yes. We are platform-agnostic and integrate with your existing simulation and data tooling rather than forcing a new tech stack.
Is this only for autonomous cars?
No. Scenario Services are designed for any physical AI product where perception and control interact with complex environments, autonomous vehicles, defense systems, industrial robots, and beyond.
How does DDD support simulation and digital twin workflows?
DDD extracts and structures scenarios from fleet logs, synthetic engines, and sensor data, and validates digital twins by aligning simulation properties with real-world environments.
Why is ODD Analysis important for autonomy?
ODD Analysis ensures systems are trained and tested within realistic operating conditions, weather, geography, traffic, lighting, and infrastructure, reducing deployment risk and improving predictability.
How do edge cases improve AI safety?
Edge cases highlight rare, critical, and unexpected events that models often fail to recognize. Incorporating these into training and testing significantly improves safety.