Quick Takeaways
- The collaboration enables high-resolution radar perception while keeping sensor and system costs under control for scalable autonomous architectures.
- Centralized AI-driven radar processing strengthens readiness for software-defined and next-generation autonomous vehicles.
On January 7, Provizio announced a collaboration with Texas Instruments to develop an advanced Provizio Texas Instruments 8x8 Satellite Imaging Radar software application built on the AWR2188 and designed for deployment on the NVIDIA platform. The initiative focuses on delivering higher-resolution radar perception while maintaining system-level cost efficiency for autonomous vehicle architectures.
The 8x8 single-chip radar configuration significantly improves angular resolution across both azimuth and elevation. By combining this with a satellite streaming architecture, the solution enables higher performance through a centralized ECU that runs AI and machine learning algorithms, reducing compute requirements and overall cost at the radar sensor level.
Provizio Texas Instruments 8x8 Satellite Imaging Radar Architecture
The satellite imaging approach separates sensing from heavy computation. Radar data is captured at the edge and streamed to a central ECU, where perception intelligence is executed.
Key architectural advantages include:
AWR2188 with Provizio Signal Processing Software
The AWR2188 radar chipset from Texas Instruments is bundled with Provizio’s enhanced signal processing software to showcase the potential of satellite radar architectures. The solution is AI-ready and optimized for Provizio’s 5D Perception Neural Network.
Core capabilities include:
Software-Defined Radar and Active Perception
Provizio delivers the application layer of software-defined radar, accelerating active perception by tightly integrating radar sensing with AI-driven interpretation. The approach enhances situational awareness while allowing continuous improvement through software updates rather than hardware changes.
By aligning satellite radar architectures with centralized compute platforms, the collaboration aims to advance scalable, cost-effective perception systems for future autonomous mobility.
The 8x8 single-chip radar configuration significantly improves angular resolution across both azimuth and elevation. By combining this with a satellite streaming architecture, the solution enables higher performance through a centralized ECU that runs AI and machine learning algorithms, reducing compute requirements and overall cost at the radar sensor level.
Provizio Texas Instruments 8x8 Satellite Imaging Radar Architecture
The satellite imaging approach separates sensing from heavy computation. Radar data is captured at the edge and streamed to a central ECU, where perception intelligence is executed.
Key architectural advantages include:
- Higher angular resolution from the 8x8 single-chip configuration
- Centralized AI/ML processing for scalable performance
- Lower sensor-side hardware complexity and cost
- Improved readiness for software-defined vehicle platforms
AWR2188 with Provizio Signal Processing Software
The AWR2188 radar chipset from Texas Instruments is bundled with Provizio’s enhanced signal processing software to showcase the potential of satellite radar architectures. The solution is AI-ready and optimized for Provizio’s 5D Perception Neural Network.
Core capabilities include:
- Conversion of raw radar signals into high-fidelity perception data
- MIMSO processing chain for accurate object detection and tracking
- Support for both on-road and off-road autonomous vehicle scenarios
Software-Defined Radar and Active Perception
Provizio delivers the application layer of software-defined radar, accelerating active perception by tightly integrating radar sensing with AI-driven interpretation. The approach enhances situational awareness while allowing continuous improvement through software updates rather than hardware changes.
By aligning satellite radar architectures with centralized compute platforms, the collaboration aims to advance scalable, cost-effective perception systems for future autonomous mobility.
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