Extreme Traffic Behavior Quantification With Extreme Value Modeling
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Solution Overview
Problem
Current methods for modeling road user behavior, especially rare events like hard braking or abrupt turning, require extensive and costly data collection, making it impractical for efficient and reliable modeling in autonomous drive systems.
Innovation Solution
The method involves obtaining samples of road user behavior, selecting a subset that follows a pre-determined statistical extreme value distribution, parameterizing it, and quantifying behavior based on this distribution, reducing data requirements and enabling analysis of rare behaviors through metrics like time between exceedances and confidence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive data collection is used to model rare road user behaviors, then modeling accuracy is improved, but data collection cost and time increase significantly
Solution Approach 1:
The patent extracts only the extreme value samples from the complete data set. Instead of using all collected data, it selectively takes out the tail events (rare behaviors) that exceed a certain threshold. This extraction principle allows modeling to focus on the critical rare events without requiring the entire data set, thereby reducing data collection time while maintaining modeling accuracy for rare behaviors.
Solution Approach 2:
The patent changes the parameter perspective by transforming raw behavioral data into extreme value distribution parameters (such as shape parameter ξ and scale parameter σ). By parameterizing the extreme value distribution, the system can characterize rare behaviors with a small number of parameters derived from limited extreme samples, rather than requiring extensive raw data. This parameter transformation enables accurate modeling with reduced data requirements.
2Reliability
If extensive data collection is used to model rare road user behaviors, then modeling accuracy is improved, but development cost increases
Solution Approach 1:
The patent extracts only the extreme value samples from the complete data set. Instead of using all collected data, it selectively takes out the tail events (rare behaviors) that exceed a certain threshold. This extraction principle allows modeling to focus on the critical rare events without requiring the entire data set, thereby reducing data collection time while maintaining modeling accuracy for rare behaviors.
Solution Approach 2:
The patent changes the parameter perspective by transforming raw behavioral data into extreme value distribution parameters (such as shape parameter ξ and scale parameter σ). By parameterizing the extreme value distribution, the system can characterize rare behaviors with a small number of parameters derived from limited extreme samples, rather than requiring extensive raw data. This parameter transformation enables accurate modeling with reduced data requirements.
3Adaptability or versatility
If traditional behavior modeling methods are used, then comprehensive behavior coverage is achieved, but data requirements become impractical
Solution Approach 1:
The patent changes the parameter perspective by transforming raw behavioral data into extreme value distribution parameters (such as shape parameter ξ and scale parameter σ). By parameterizing the extreme value distribution, the system can characterize rare behaviors with a small number of parameters derived from limited extreme samples, rather than requiring extensive raw data. This parameter transformation enables accurate modeling with reduced data requirements.
Solution Approach 2:
The extreme value distribution serves as an intermediary mathematical model between the raw behavioral data and the final behavior predictions. This intermediary distribution (GEV or GPD) acts as a bridge that can be parameterized from limited extreme samples and then used to generate synthetic extreme behaviors, thereby achieving comprehensive behavior coverage without requiring impractical amounts of raw data.
Data Source
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AI summary
A method for quantifying road user behavior, the method comprising; obtaining samples of road user behavior selecting a subset of the road user behavior samples such that the selected samples follow a pre-determined statistical extreme value distribution, parameterizing the pre-determined statistical extreme value distribution based on the selected samples of road user behavior, and quantifying road user behavior based on the parameterized statistical extreme value distribution.