Agricultural Vehicle Imaging Controller for Obstacle Detection
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Solution Overview
Problem
Agricultural vehicles face challenges in navigating complex and unpredictable agricultural environments due to obstacles like bumps, rocks, animals, and other vehicles, which can be hidden from the operator's view, leading to potential collisions.
Innovation Solution
The integration of cameras and LiDAR sensors with an imaging controller that uses neural networks to analyze image and LiDAR data, fusing the information to detect agricultural objects and obstacles, and control the vehicle's operations to avoid collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the operator relies on visual observation to detect obstacles, then the system remains simple, but the detection precision and reliability are insufficient in agricultural environments with hidden or moving obstacles
Solution Approach 1:
The patent combines multiple sensors (cameras, LiDAR) with neural network processing systems to create an integrated obstacle detection system. This merging of components enables precise detection of agricultural objects while managing system complexity through coordinated operation of the fused sensor data and AI processing.
Solution Approach 2:
The patent replaces manual visual observation with automated sensor-based detection systems. Cameras and LiDAR sensors, combined with neural networks, substitute the human operator's visual system, providing continuous automated monitoring without requiring manual vigilance.
2Reliability
If multiple sensors and neural networks are integrated to detect obstacles, then the object detection precision improves, but the device complexity increases
Solution Approach 1:
The patent merges data from multiple sensors (cameras, LiDAR) and processes them through neural networks to achieve reliable obstacle detection. This combination allows the system to detect and track agricultural objects with high reliability by cross-validating information from different sensor types.
Solution Approach 2:
The neural network acts as an intermediary that processes and fuses data from multiple sensors. This intermediary layer manages the complexity by automatically integrating information from cameras and LiDAR, transforming raw sensor data into reliable object detection results without requiring manual system management.
3Productivity
If the vehicle operates at higher speeds to increase productivity, then the output improves, but the time available to detect and avoid obstacles decreases
Solution Approach 1:
The system performs preliminary detection and tracking of agricultural objects using sensors and neural networks before the vehicle reaches potential collision points. This advance detection allows the vehicle to maintain higher speeds while still having sufficient time to react to obstacles, as the system continuously monitors and identifies objects in the path ahead.
Solution Approach 2:
The patent implements continuous feedback through sensor monitoring and neural network processing, providing real-time information about detected objects. This feedback loop enables the vehicle to adjust its speed and trajectory dynamically, maintaining high productivity while ensuring adequate reaction time to avoid obstacles based on current environmental conditions.
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
This solution enables precise object detection and tracking, allowing the vehicle to operate safely and avoid obstacles, even in adverse weather conditions or when objects are obstructed from view.
Implementation Method 1
a LiDAR sensor operably coupled to the agricultural vehicle
Implementation Method 2
receive LiDAR data from the LiDAR sensor
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
An agricultural vehicle includes cameras and a LiDAR sensor operably coupled to the agricultural vehicle, and an imaging controller operably coupled to the LiDAR sensor and the cameras. The imaging controller includes at least one processor, and instructions that cause the processor to receive image data from the cameras, receive LiDAR data from the LiDAR sensor, analyze the image data from each of the cameras to generate labeled image data, analyze the LiDAR data to generate labeled LiDAR data, and fuse the labeled image data with the labeled LiDAR data. Related agricultural vehicles and methods are also disclosed.