Special Road Condition Recognition Using Vehicle Parameter Data
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Solution Overview
Problem
Conventional methods for recognizing special road conditions in vehicles suffer from poor real-time performance, as they often fail to alert drivers or autonomous systems in time due to high-speed vehicle passage and rely on incomplete or inaccurate image recognition.
Innovation Solution
A method and apparatus that utilize real-time vehicle parameter data from multiple sources to identify and mark special road conditions on maps, allowing for advanced recognition and timely alerts by determining the presence of special road conditions based on vehicle parameters and road condition models, enabling improved accuracy and real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition methods are used to identify special road conditions, then recognition capability is provided, but real-time performance deteriorates due to high-speed vehicle passage
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing vehicle parameter data before the vehicle actually encounters the special road condition. The server receives vehicle parameters from multiple vehicles, identifies special road conditions, and sends warning information to vehicles before they reach the hazardous area, enabling proactive rather than reactive recognition.
Solution Approach 2:
The patent introduces a server as an intermediary between vehicles and special road conditions. The server acts as a central processing hub that collects vehicle parameter data, performs recognition analysis, and distributes warning information to multiple vehicles, enabling real-time recognition without requiring complex onboard image recognition systems in each vehicle.
2Measurement precision
If conventional image recognition is used, then special road conditions can be identified, but accuracy deteriorates due to incomplete or inaccurate image data
Solution Approach 1:
The patent merges data from multiple vehicles by collecting vehicle parameter data from several vehicles that have passed or are passing through the same road section. The server综合分析 this aggregated data to identify special road conditions, which improves both accuracy and reliability compared to using data from a single vehicle's image recognition system.
Solution Approach 2:
The patent implements feedback by continuously receiving vehicle parameter data from multiple vehicles and using this information to identify and warn about special road conditions. The system learns from the collective experience of multiple vehicles, improving recognition accuracy over time through feedback from actual driving conditions.
Data Source
AI summary
An example apparatus includes at least one processor and at least one memory coupled to the at least one processor and storing programming instructions for execution by the at least one processor to: obtain map data at a current moment, where the map data includes a first road area at the current moment, the special road condition is located in the first road area, the first road area is obtained based on a road condition model and a vehicle parameter within a preset time period before the current moment, and the road condition model represents a correspondence between a feature of the vehicle parameter and the special road condition; and determine, based on a planned route of a vehicle, that a second road area exists in the planned route of the vehicle, where the second road area is in the first road area.