Driving Risk Monitoring Using Vehicle Overlap Regions
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Solution Overview
Problem
Existing methods for evaluating driving behavior risk degrees in intelligent vehicles have different information requirements that are difficult to uniformly and effectively transfer, making it challenging to assess the risk of surrounding traffic participants during the driving process.
Innovation Solution
A method and device that acquire vehicle size information, driving information, and environmental information to determine the number of overlapping regions and risk degree, representing collision scenarios in a two-dimensional plane, facilitating risk evaluation and collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different information requirements are used for risk evaluation in particular regions, then the evaluation can be tailored to specific scenarios, but the requirements cannot be uniformly and effectively transferred across different scenarios
Solution Approach 1:
The patent creates a universal risk evaluation framework that can be applied across multiple scenarios (intersections, highways, residential areas, etc.) by defining a standardized set of sensing layer information requirements. This framework enables the same evaluation method to be transferred uniformly to different scenarios while maintaining scenario-specific adaptability through configurable risk factors and weighting parameters.
Solution Approach 2:
The patent adjusts evaluation parameters such as risk factors, weighting coefficients, and threshold values based on different scenario characteristics. By changing these parameters rather than the fundamental evaluation methodology, the system achieves scenario-specific adaptability while maintaining uniform information requirements across all scenarios.
2Adaptability or versatility
If uniform information requirements are imposed on all scenarios, then transferability is improved, but the ability to effectively evaluate specific scenario risks is reduced
Solution Approach 1:
The patent establishes a universal information requirement framework that ensures consistent data collection across all scenarios. This framework includes standardized sensing layers (vehicle sensors, road infrastructure, environment, traffic participants) that can be uniformly implemented while maintaining the ability to accurately evaluate specific scenario risks through scenario-configurable parameters.
Solution Approach 2:
The patent applies local quality by allowing different weighting factors and risk thresholds for different scenarios while maintaining uniform information collection. For example, speed-related risks may be weighted higher on highways, while pedestrian-related risks are weighted higher in residential areas, achieving scenario-specific precision without sacrificing transferability.
3Reliability
If multiple sensing layer information requirements are collected, then comprehensive risk evaluation is achieved, but the complexity of information processing and transfer increases
Solution Approach 1:
The patent segments the information requirements into distinct sensing layers (vehicle sensors, road infrastructure, environment, traffic participants). Each layer is independently defined and can be processed separately, then integrated through a unified evaluation framework. This segmentation reduces overall complexity by organizing information systematically while maintaining comprehensive risk evaluation capability.
Data Source
AI summary
The present disclosure discloses a method for monitoring driving behavior risk degree, including: acquiring first vehicle size information of a first vehicle, second vehicle size information of a second vehicle, vehicle driving information and vehicle driving environment information; determining the number of overlapping regions of the first vehicle and the second vehicle based on the first vehicle size information, the second vehicle size information and the vehicle driving information; and determining risk degree information of driving behavior based on the number of overlapping regions, the vehicle driving information and the vehicle driving environment information. This method may be applied in multiple scenarios, and facilitates drivers or passengers in avoiding them from traffic collision incidents, so that the safeties of the drivers and the passengers is greatly improved.