Intelligent Driving Scenario Recognition Beyond Image-Only Classification
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Solution Overview
Problem
Existing intelligent driving systems face challenges in accurately recognizing driving scenarios, particularly in adverse weather conditions such as rain or fog, leading to reduced accuracy and effective operation.
Innovation Solution
An intelligent driving method that utilizes structured semantic information, road attributes, and traffic status spectrum to recognize driving scenarios by comparing current vehicle parameters with a scenario feature library, determining a total similarity based on multiple dimensions, and adjusting vehicle control accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a convolutional neural network classifier is used to recognize driving scenarios from real-time perceived images, then the system can automatically identify driving scenarios, but the recognition accuracy decreases in adverse weather conditions such as rain, fog, or poor lighting
Solution Approach 1:
The patent segments the scenario recognition task into multiple independent modules: structured semantic information extraction module, road attribute recognition module, and traffic status spectrum analysis module. Each module processes specific aspects of the driving environment independently, then their results are integrated to achieve comprehensive scenario recognition. This segmentation allows the system to bypass the limitations of single-image convolutional neural networks by gathering information from multiple sources that are less affected by adverse weather conditions.
2Loss of information
If real-time perceived images are used for scenario recognition, then visual information is obtained, but the information is unclear and unreliable in adverse weather conditions
Solution Approach 1:
The patent merges multiple information sources including structured semantic information from sensors, road attribute data from maps and positioning systems, and traffic status spectrum from radar and communication systems. By combining these diverse information sources, the system creates a more complete and reliable representation of the driving scenario that compensates for the deficiencies of visual information alone in adverse weather conditions.
Solution Approach 2:
The patent introduces structured semantic information as an intermediary that translates complex sensor data and environmental conditions into standardized, interpretable features. This intermediary representation allows the system to process and integrate information from multiple sources in a unified framework, improving the reliability of scenario recognition by reducing the direct dependence on degraded visual input.
3Measurement precision
If picture-based recognition is used for scenario classification, then visual pattern matching is performed, but the calculation complexity increases and accuracy decreases
Solution Approach 1:
The patent replaces the mechanical image processing approach of convolutional neural networks with a structured information processing system. Instead of performing complex pixel-level computations, the system extracts structured semantic information, road attributes, and traffic status spectra, then compares these structured features against predefined scenario templates. This substitution dramatically reduces calculation complexity while maintaining or improving recognition accuracy.
Data Source
AI summary
An intelligent driving method comprising: obtaining feature parameters of a vehicle at a current moment and a road attribute of a driving scenario of the vehicle in a preset future time period; comparing the feature parameters at the current moment with feature parameters of a standard scenario in a scenario feature library; comparing the road attribute of the driving scenario of the vehicle in the preset future time period with a road attribute of the standard scenario in the scenario feature library; determining a total similarity of each scenario class to a driving scenario of the vehicle at the current moment based on comparing results; determining, as the driving scenario at the current moment, a first scenario class with a highest total similarity in N scenario classes; and controlling, based on the determining result, the vehicle to perform intelligent driving.