Data collection drives safer, smarter mobility.

To develop perception that performs beyond the test track, our team operates dedicated data collection vehicles equipped with our infrared technology and supporting sensor suite. These vehicles are deployed across multiple environments to capture representative scenarios under day, night, weather affected, and low visibility conditions.

Why It Matters Cars that capture real‑world driving.
Compal’s data collection vehicles record everyday road conditions to power safer perception. Real-World Safety Collecting insights from every journey.
Our fleet gathers diverse scenarios—weather, lighting, and traffic—to strengthen safety system reliability. Why It Matters Driving data into smarter mobility.
Compal’s cars provide the foundation for testing and validation, ensuring technology reflects real roads.

Data collection vehicles provide the foundation for trusted performance.

  • Vehicle Platform
  • Deployed across the U.S., Europe, and Asia (Japan, Taiwan, Korea)
  • Configurable for regional test requirements and local driving environments
  • We actively collect data in:
  • Nighttime and 0-lux environments
  • Tunnel exits and high-glare transitions
  • Fog, rain, snow & environmental diffusion
  • Rural low-light zones
  • Pedestrian and cyclist interactions
  • Stationary and partially occluded obstacles
  • Construction areas and unexpected roadside objects
Data Collection Privacy Notice
  • Captured data supports
  • AI training & performance benchmarking
  • Sensor fusion model development
  • Regulatory alignment (FMVSS 127, Euro NCAP)
  • Predevelopment evaluation ahead of OEM integration

Global data visibility ensures reliable performance.

Data distribution plays a key role in traceability and AI model reliability. To support this, we are developing a web-based dashboard that provides an overview of global training and labeling statistics. The platform is designed for internal performance monitoring and can be presented to customers as a professional insight into dataset quality and coverage.

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