Roadside Development Estimation via Region Segmentation
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Solution Overview
Problem
Existing methods for estimating roadside development progression require significant computing capacity and memory, making them inefficient and resource-intensive, especially when calculating multiple trajectories.
Innovation Solution
A method that determines support points for estimating roadside development progression by dividing the vehicle's surroundings into regions based on detected obstacles, allowing for efficient and real-time calculation without the need for trajectory calculations, using a control unit to ascertain and define support points and regions for improved estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If trajectory-based estimation methods are used to estimate roadside development, then estimation accuracy is improved, but computing capacity and memory requirements increase significantly
Solution Approach 1:
The method segments the surrounding area into multiple regions based on support points detected from obstacles. Instead of calculating all possible trajectories, the system divides the space into manageable regions and estimates roadside development separately for each region, significantly reducing computational complexity while maintaining estimation accuracy.
Solution Approach 2:
The invention extracts only the essential elements needed for estimation by identifying support points from detected obstacles. Rather than processing complete trajectory data, the system extracts key positional information from obstacles to define support points, which are then used to determine regions and estimate roadside development progression without requiring full trajectory calculations.
2Reliability
If multiple trajectories are calculated for roadside development estimation, then estimation robustness is improved, but memory requirements increase significantly
Solution Approach 1:
The method divides the estimation problem into segmented regions based on support points. Each region is processed independently to determine local roadside development characteristics, reducing the memory needed to store trajectory data while maintaining robustness through multi-region analysis.
Solution Approach 2:
Instead of calculating all possible trajectories throughout the entire surrounding area, the system performs partial action by calculating trajectories only within specific regions defined by support points. This selective approach maintains estimation robustness while significantly reducing memory requirements.
3Loss of information
If trajectory calculations are performed for real-time roadside development estimation, then estimation completeness is improved, but processing speed decreases
Solution Approach 1:
The system segments the surrounding area into regions based on support points and processes each region independently. This segmentation enables parallel processing of multiple regions, improving real-time processing speed while maintaining complete estimation coverage through systematic region-by-region analysis.
Solution Approach 2:
The method performs preliminary action by first detecting obstacles and identifying support points before proceeding to region definition and trajectory calculation. This preliminary identification of key elements prepares the data structure in advance, enabling faster real-time processing when actual roadside development estimation is needed.
Data Source
AI summary
A method determines support points for estimating a progression of roadside development of a road. The method determines a position of a first support point in the surroundings of a vehicle; determines a plurality of regions in a travel direction and/or counter to the travel direction of the vehicle on the basis of the position of the first support point; and determines support points of roadside development for each of the determined regions in the travel direction, counter to the travel direction and left and right of the vehicle.