The application of machine learning in automotive radar systems presents severe challenges, particularly due to the limited availability of raw radar data tailored to specific radar configurations and annotated datasets. In this presentation, we introduce a novel Python-based framework designed to address these challenges by enabling large-scale radar data generation and visualization.

Our framework leverages existing radar detections from production systems, accumulating radar detections over multiple cycles to enhance resolution and minimize feature fluctuation. These accumulated features, referred to as pseudo scatter points, are treated as scatter centers to generate raw spectra for virtual radar systems with arbitrary antenna arrangements. This approach incorporates clutter in the simulation to achieve more representative results.

Key features of our framework include:

• GPU Acceleration: Utilizes GPU acceleration to handle the computational demands of large-scale radar data generation efficiently.
• Inbuilt Visualizer: Provides an inbuilt visualizer for radar data, facilitating real-time analysis and debugging.
• Specialized Data class: Implements a specialized data class to streamline the process of radar data generation and processing.