Multi-Modal Object Detection for Filtering Radar False Positives
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Solution Overview
Problem
Conventional object detection systems in agricultural vehicles, relying solely on radar, struggle to differentiate between drivable and non-drivable objects, leading to unnecessary stops due to false positives, such as detecting small objects like corn stalks or elevation changes, which can cause performance issues.
Innovation Solution
A multi-modal system integrating radar, camera, and 3D sensors with an AI/ML model that generates augmented radar detection data by projecting drivable area models into 3D space, correlating radar targets with 3D non-drivable targets to reduce false positives and maintain safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional radar detection is used to detect objects in the navigation path, then objects can be detected, but false positives occur causing unnecessary vehicle stops
Solution Approach 1:
The patent combines radar detection with camera imaging and 3D sensor data to create a multi-modal detection system. The radar provides initial object detection while the camera and 3D sensors provide contextual information to verify whether detected objects are truly obstacles, thereby reducing false positives while maintaining detection reliability.
Solution Approach 2:
The patent introduces an AI/ML model as an intermediary that processes radar detection data and compares it with camera and 3D sensor data. This intermediary system determines whether radar-detected objects are actual obstacles or false positives (such as corn stalks or elevation changes), preventing unnecessary vehicle stops.
2Measurement precision
If radar detects all objects in the path, then detection sensitivity is high, but discrimination between drivable and non-drivable objects fails
Solution Approach 1:
The patent segments the detection process into multiple stages: radar provides initial object detection, camera provides visual characteristics, 3D sensors provide spatial information, and AI/ML models integrate these segmented data sources to determine drivability, thereby preserving object characteristics information that radar alone cannot provide.
Solution Approach 2:
The patent adds dimensional information by incorporating camera imaging and 3D sensor data alongside radar detection. This multi-dimensional approach provides contextual information about object characteristics, elevation, and spatial relationships, enabling discrimination between drivable and non-drivable objects while maintaining high detection sensitivity.
Data Source
AI summary
Techniques for multi-modal contextualization of object detection for use with an agricultural vehicle are described herein. The techniques can provide additional context information to radar detection to assess the likelihood of an object detected by radar of being an object of interest. Examples of detection that might be detected by a radar sensor that, based on the additional context information, the agricultural vehicle can make a determination to ignore can include ground targets, ghost targets, side or overhead reflections from obstacles, detections from tall crops/weeds in a field.