Customer Pain Points
Long-tail Rarity & Overseas Collection Challenges
Difficulty in acquiring overseas/edge/rare scenarios leads to insufficient training data coverage
High Cross-Platform Migration Costs
Inability to reuse data across different vehicle models/generations results in high recollection costs
Synthetic-Real Data Discrepancy
Significant style differences between synthetic and real data cause poor model transfer performance
Labeling Intensity and Consistency Issues
Labor-intensive labeling processes with long cycles and difficult quality consistency maintenance
Product Value
Novel View Synthesis (4DGS)
Generates consistent image sequences for new vehicle models/viewpoints through dynamic 3DGS + Diffusion technology
Multimodal Data Generation
Supports comprehensive training annotation outputs including: <br /> images + depth maps + semantic segmentation + 2D/3D bounding boxes + point clouds
World Model-Enhanced Consistency & Realism
Integrates physical priors and multimodal constraints through world modeling, significantly improving consistency between images, semantics, and geometry to generate training samples closer to real-data distribution
Automated Pipeline with High Controllability & Asset Reusability
Enables parametric adjustment of weather, time, traffic density and other variables, combined with structured scenario configuration for rapid batch generation of diverse, fully-annotated datasets

Core Capabilities

01

High-Fidelity 3D Simulation Toolchain & Massive Asset Library

Traffic Props: 6,000+ elements including traffic signs/signals/road markings/planters/pedestrian paths/fences Vehicles: 200+ models covering trucks/MPVs/sedans/SUVs/special vehicles/tricycles/e-bikes/bicycles Vegetation: 1,000+ trees across 30+ species SOTIF Elements: Road debris/spills/abnormal vehicle markings/states/pedestrian clothing/actions

02

Fully Automated Perception Test Scenario Generation

Programmatic modeling and scenario generation dramatically reduces development costs Generates perception-focused simulation scenarios from real HD road networks Automatically creates roads/surroundings/buildings/vegetation/overpasses/water bodies/night scenes Configurable styles for urban/rural/highway environments

03

Physics-Based Sensor Simulation Models

Lab-calibrated camera parameters Extensive sensor parameter simulation Environmental factors (physics-based lighting, complex weather systems) Abnormal conditions (occlusions/dirt/liquid splashes)

Application Scenarios
01

Overseas Scenario Data Supplement for Global Vehicle Models

Automatically generates diverse overseas road environments (e.g., signage systems, architectural styles, traffic behaviors) to address data gaps for global market expansion, reducing reliance on costly physical data collection abroad.
02

Corner Case Replay for Enhanced Model Training

Uses synthetic data to recreate rare or hazardous scenarios (e.g., sudden pedestrian crossings, extreme weather) for safe and repetitive model reinforcement training, improving algorithm robustness.
03

Cross-Platform Data Migration for New Vehicle Models

Generates multi-sensor data (cameras, LiDAR) tailored to new vehicle configurations (sensor placement, parameters), enabling efficient knowledge transfer without recollecting real-world data.
04

Hybrid Training with Real and Synthetic Data for Improved Generalization

Combines real-world data with style-consistent synthetic data to expand dataset diversity, enhancing model adaptation to unseen environments while maintaining real-data performance.
Cases
Synthetic Parking Scenario Dataset for a Sino-foreign Joint Venture of a Southwest Automotive Group
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