Agricultural Access Point Detection From Vehicle Movement Patterns
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Solution Overview
Problem
Existing agricultural route planning techniques fail to accurately identify access points to agricultural regions, leading to inefficiencies in fuel consumption and soil compaction, and do not account for vehicle movements and loading/unloading activities.
Innovation Solution
A computer-implemented method that processes region and vehicular location information to identify access points by analyzing vehicular movement patterns, including speed and turn indicators, to accurately detect entry/exit points and loading/unloading locations using machine-learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional route planning techniques are used, then route generation is simple, but access point identification is inaccurate
Solution Approach 1:
The system performs preliminary actions by collecting and storing vehicular location data, speed data, and turn indicator data before route planning. This pre-collected data is then used to accurately identify access points, improving measurement precision without adding complexity to the actual route generation process.
Solution Approach 2:
The patent introduces an intermediary processing layer that analyzes vehicular behavior data (location, speed, turn indicators) to identify access points. This intermediary system acts as a mediator between raw vehicle data and route planning, improving access point identification accuracy while keeping the core route planning system relatively simple.
2Measurement precision
If more vehicular data is collected and processed, then access point detection accuracy improves, but processing complexity increases
Solution Approach 1:
The patent segments the data processing into distinct components: collecting vehicular location data, collecting speed data, collecting turn indicator data, and then processing these segmented data types together to identify access points. This segmentation makes the complex processing task more manageable and systematic.
Solution Approach 2:
The system uses feedback from multiple data sources (location, speed, turn indicators) to continuously refine access point identification. By processing vehicular behavior patterns and using this feedback to confirm or adjust access point locations, the system improves detection accuracy while managing processing complexity through iterative refinement.
3Loss of energy
If accurate access point identification is implemented, then fuel efficiency improves, but system complexity increases
Solution Approach 1:
The system performs preliminary data collection and access point identification before route planning, so that when routes are generated, they can directly utilize pre-identified access points. This reduces the need for complex real-time calculations during route execution, improving fuel efficiency while managing system complexity through advance preparation.
4Measurement precision
If vehicle movement patterns are analyzed, then route planning accuracy improves, but processing time increases
Solution Approach 1:
The patent collects and processes vehicular movement pattern data in advance, before route planning is executed. By pre-analyzing location, speed, and turn indicator data to identify access points, the system reduces the processing time required during actual route planning, while maintaining high route planning accuracy through the use of pre-processed behavioral patterns.
Data Source
AI summary
A mechanism for identifying access points on a boundary of an agricultural region. The access points are identified by processing region location information and vehicular location information. The region location information identifies the location of the boundary. The vehicular location information identifies locations of agricultural vehicles.