Multi-Sensor Object Detection Using Radar and Machine Learning
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Solution Overview
Problem
Existing object detection systems, such as LIDAR and vision systems, are vulnerable to adverse weather conditions and sensor contamination, leading to unreliable performance in various environmental scenarios.
Innovation Solution
A method and system utilizing radar data combined with LIDAR and vision data, processed by machine learning algorithms to detect objects and features like type, location, distance, orientation, size, and speed, which selects the most accurate data based on weather conditions and ambient light levels, enhancing object detection reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIDAR and vision systems are used for object detection, then color information and three dimensional detection capabilities are provided, but vulnerability to adverse weather conditions and sensor contamination persists
Solution Approach 1:
The patent combines radar data with LIDAR and vision system data to create a multi-sensor object detection system. The radar system provides reliable detection in adverse weather conditions while LIDAR and vision systems contribute to three-dimensional detection and color information, resolving the contradiction between detection accuracy and reliability in weather conditions.
2Reliability
If radar data is used for object detection, then resistance to environmental interference is provided, but detection of color information and fine surface details is limited
Solution Approach 1:
The patent merges radar data with LIDAR and vision system data where radar provides reliable detection in adverse weather while LIDAR and vision systems supplement with color information and fine surface details, resolving the contradiction between reliability and measurement precision.
3Adaptability or versatility
If multiple sensor systems (radar, LIDAR, vision) are integrated, then detection capabilities are enhanced, but system complexity increases
Solution Approach 1:
The patent creates a universal object detection system that can adapt to different weather conditions and detection requirements by integrating multiple sensor types. The system selectively uses radar, LIDAR, and vision data based on environmental conditions, providing multi-functionality that resolves the contradiction between adaptability and complexity.
4Measurement precision
If machine learning algorithms are applied to process multi-sensor data, then object detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies machine learning algorithms to pre-process and fuse data from multiple sensors before object detection. By performing preliminary data fusion and feature extraction, the system improves detection accuracy while reducing the computational burden and processing time required for subsequent analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides robust and accurate object detection in adverse weather conditions by leveraging radar's resistance to environmental interference and integrating data from multiple sensors, improving detection capabilities and reliability.
Implementation Method 1
A scanning radar or combination of radars mounted on a vehicle or moving object scans the environment to acquire information
Implementation Method 2
receive further data of the environment which was scanned from one or both of a LIDAR system or a vision system
Data Source
AI summary
A method and system for using one or more radar systems for object detection based on machine learning in an environment is disclosed. A scanning radar or combination of radars mounted on a vehicle or moving object scans the environment to acquire information. The radar data may be a 3D point cloud, 2D radar image or 3D radar image. The radar data may also be combined with data from LIDAR, vision or both. A machine learning algorithm is then applied to the acquired data to detect dynamic or static objects within the environment, and identify at least one object feature comprising one of a type, location, distance, orientation, size or speed of an object.