Vehicle Scenario Complexity Detection Using Static and Dynamic Factors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining the complexity of a vehicle's traveling scenario fail to account for the traveling status of surrounding vehicles, leading to incomplete reflection of the actual scenario complexity and potential safety risks in self-driving cars.
Innovation Solution
A method that combines static and dynamic factors to determine the complexity of a vehicle's scenario, using in-vehicle radar to detect the traveling speed of surrounding vehicles and GPS/high-definition maps for static information, calculating dynamic and static complexities, and integrating them to obtain a comprehensive complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complexity is determined only based on static factors and vehicle speed, then the determination method is simple, but the complexity cannot fully reflect the actual traveling scenario complexity
Solution Approach 1:
The complexity determination is segmented into two independent parts: static complexity (from road geometry, lane configuration, and environment) and dynamic complexity (from surrounding vehicle interactions). Each part is calculated separately using different data sources and methods, then combined to form the comprehensive complexity metric. This segmentation allows the system to maintain measurement precision while managing computational complexity through modular processing.
Solution Approach 2:
The system transitions from a purely static complexity model to a dynamic model that continuously updates based on real-time vehicle interactions. The dynamic complexity component captures temporal changes in the traveling scenario by monitoring relative positions, speeds, and maneuvers of surrounding vehicles, making the complexity determination adaptive to changing conditions rather than fixed based on pre-stored static data.
2Reliability
If the traveling status of surrounding vehicles is not considered, then the calculation is simpler, but the safety risk increases due to incomplete scenario reflection
Solution Approach 1:
The patent introduces an intermediary dynamic complexity calculation layer that processes surrounding vehicle data and translates it into a complexity metric. This intermediary layer acts as a bridge between raw sensor data from multiple vehicles and the final complexity determination, filtering and synthesizing the information in a structured way that maintains safety requirements while managing computational load through organized data processing.
Solution Approach 2:
The system performs preliminary classification and filtering of surrounding vehicle data before full complexity calculation. By pre-identifying relevant vehicles and their critical parameters (relative position, speed differential, lane position), the system prepares the data in advance for more efficient processing, reducing the computational burden during real-time complexity determination while ensuring all safety-critical factors are captured.
Data Source
AI summary
A method for detecting a complexity of a traveling scenario of a vehicle includes obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle, determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located, determining static information of each static factor in the traveling scenario in which the vehicle is currently located, obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located, and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.