Road Scene Likelihood Classification for Reliable Road Type Detection
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Solution Overview
Problem
Existing road type determination technologies struggle to reliably differentiate between ordinary roads and limited-access roads, and fail to determine road type when traffic lights are not detected, leading to potential errors in driving assistance systems.
Innovation Solution
A road type determination apparatus that acquires image information of the travel road, determines the traveling scene, calculates likelihoods for ordinary roads and limited-access roads based on the scene, and determines the road type accordingly, enabling more reliable differentiation between road types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If road type determination is based on traffic light detection, then determination can be made when traffic lights are present, but determination fails when traffic lights are not detected
Solution Approach 1:
The road type determination is segmented into multiple independent determination methods: traffic light detection method, road marking detection method, and roadside facility detection method. Each method operates independently to determine road type based on different visual features, ensuring that determination can proceed even when one method is not applicable
Solution Approach 2:
The determination apparatus is designed with multi-functionality to handle various road scenes through multiple determination methods. The system can universally apply different detection strategies (traffic lights, road markings, roadside facilities) depending on the scene characteristics, making it adaptable to both limited-access roads and ordinary roads regardless of traffic light presence
2Measurement precision
If road type determination uses simple methods, then the system is easy to operate, but the differentiation between ordinary roads and limited-access roads is unreliable
Solution Approach 1:
The determination system dynamically selects and combines multiple determination methods based on scene conditions. The system evaluates the availability and reliability of different detection targets (traffic lights, road markings, roadside facilities) and adaptively weights their contributions to the final road type determination, achieving high accuracy without requiring a permanently complex system structure
Solution Approach 2:
The determination apparatus introduces intermediate processing steps including scene analysis, target detection, and result integration. These intermediary processes bridge the gap between simple image capture and accurate road type differentiation, systematically combining multiple detection results to achieve reliable determination while maintaining operational simplicity
Data Source
AI summary
In a road type determination apparatus, an image information acquiring unit acquires image information in which an image of a travel road on which a vehicle is traveling is captured. A scene determining unit determines a traveling scene of the travel road based on the image information. An ordinary road likelihood calculating unit calculates an ordinary road likelihood that indicates that a type of the travel road of the vehicle is an ordinary road based on the traveling scene. A limited-access road likelihood calculating unit calculates a limited-access road likelihood that indicates that the type of the travel road of the vehicle is a limited-access road based on the traveling scene. A type determining unit determines the type of the travel road of the vehicle based on the ordinary road likelihood and the limited-access road likelihood.