A system for learning-based processing of radar data

By applying machine learning algorithms to process raw radar data and combining sensor data, the limitations of current radar systems are addressed, enhancing performance and robustness in vehicles.

JP2026522205APending Publication Date: 2026-07-07ZENDAR INC
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Patent Information

Application Number
JP2025568236
Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
Priority Date
2023-05-25
Filing Date
2024-05-24
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Current radar systems in vehicles process data independently, leading to limited performance and computational ambiguity, especially in harsh climatic conditions, and rely on assumptions that ignore the complexity of the real world, resulting in significant information loss.

Method used

Implementing machine learning algorithms, such as deep neural networks, to process raw radar data without calculating point clouds, combining data from multiple sensors, and using automated labeling techniques to enhance performance and robustness.

Benefits of technology

Improves radar system performance by accurately determining target characteristics and generating scene representations, overcoming limitations of conventional methods in challenging environments.

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Abstract

This disclosure provides a system for processing radar data. The system may comprise a vehicle located in an environment, a radar module associated with the vehicle, and an electronic processor configured to: receive an incoming radar signal from the radar module, including an indication of an object in the environment; process the incoming radar signal through one or more signal processing algorithms to determine a raw radar spectrum; process the raw radar spectrum through a machine learning computation model to determine a set of predicted environmental outputs; determine a representative model of the environment based at least in part on the set of predicted environmental outputs; and provide the representative model of the environment to an autonomous driving system.
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