Service Overview
Physical AI
Physical AI service implementing and validating Vision-Language Models (VLM) for autonomous driving and robotics. Covers the full journey from data collection and infrastructure to AI model validation in real-world manufacturing and vehicle scenarios — delivered by a dual team spanning Japan (onsite) and Vietnam (offshore) to support the Japanese OEM's Physical AI capability roadmap.
98%+
object detection accuracy
<100ms
inference latency
30+ fps
processing throughput
99.5%+
system uptime
500+
vehicles deployed
Capabilities
Key capabilities
Multi-Modal Perception
Fusion of vision, LiDAR, and radar for comprehensive environmental understanding and object detection.
Real-Time Scene Understanding
Semantic segmentation and scene interpretation enabling intelligent decision-making in complex environments.
Autonomous Navigation
Intelligent path planning and obstacle avoidance for autonomous vehicles and mobile robots.
Robotic Manipulation
Precise control and decision-making for robotic arms and manufacturing systems.
Edge-Based Inference
Sub-100ms latency inference enabling real-time autonomous operation without cloud dependency.
Technology
Technology stack
| Component | Technology | Purpose |
|---|---|---|
| Vision | VLM, Computer Vision | Scene understanding |
| Sensor Fusion | LiDAR, Radar, Camera | Multi-modal perception |
| Robotics | ROS, Motion Planning | Robot control and navigation |
| Edge Computing | NVIDIA Orin, Qualcomm | Real-time inference |
| Development | Python, C++, CUDA | Implementation |
How we work
Implementation approach
Phase 1: Perception System Design
- Define sensor configuration and placement
- Design multi-modal fusion architecture
- Plan edge computing infrastructure
Phase 2: Model Development
- Develop VLM models for scene understanding
- Train object detection and segmentation models
- Optimize models for edge hardware
Phase 3: Integration & Testing
- Integrate with vehicle/robot control systems
- Conduct real-world testing in target environments
- Validate safety and performance metrics
Phase 4: Deployment & Optimization
- Deploy to autonomous vehicle/robot fleet
- Monitor performance and collect data
- Continuously improve models based on real-world data
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