Crash Responsibility Scoring Using Trajectory Segmentation
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Solution Overview
Problem
Current systems for crash analysis are inadequate in determining crash responsibility between vehicles using minimal sensor data, failing to provide a detailed analysis and assist in identifying the fault vehicle during a collision.
Innovation Solution
A method and system that receive GPS and acceleration samples to generate trajectories, segment them into macro and micro levels, compute scores based on acceleration data, and determine a crash responsibility score to facilitate analysis and identify fault vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed crash analysis is performed using multiple sensors, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and utilizes only the essential data elements (GPS coordinates, acceleration values, timestamps) from crash events, filtering out unnecessary information. This allows achieving precise crash responsibility determination using minimal sensor data rather than requiring comprehensive multi-sensor systems.
Solution Approach 2:
The patent creates virtual replicas of crash scenarios by processing and storing digital copies of crash data (GPS trajectories, acceleration profiles) in databases. These digital copies enable repeated analysis without requiring physical re-testing or additional sensors on vehicles.
2Loss of information
If comprehensive crash data is collected and stored, then information completeness is improved, but loss of time in data processing increases
Solution Approach 1:
The patent performs preliminary processing of crash data by immediately extracting key parameters (GPS points, acceleration values) and structuring them in standardized formats at the time of crash occurrence. This preliminary organization enables rapid retrieval and analysis without requiring extensive post-crash data processing.
Solution Approach 2:
The patent extracts only the critical data elements necessary for crash responsibility determination (trajectory points, acceleration profiles, timestamps) while discarding redundant information. This selective extraction maintains essential information completeness while dramatically reducing data processing requirements.
3Ease of operation
If subjective analysis by human experts is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent implements an automated scoring system that provides objective feedback through calculated crash responsibility scores based on processed sensor data. The system compares actual crash trajectories and acceleration profiles against expected patterns, generating quantifiable results that reduce subjectivity while maintaining operational simplicity through automated decision support.
Solution Approach 2:
The patent replaces subjective human expert analysis with automated computational algorithms that process crash data and generate responsibility scores. This substitution eliminates human bias and subjectivity while maintaining ease of operation through automated systems that require minimal manual intervention.
Data Source
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AI summary
Disclosed is a method and system for crash analysis of one or more vehicles involved in a crash is disclosed. The method may comprise capturing data samples such as a plurality of GPS samples and a plurality of acceleration samples. The method may further comprise generating a trajectory. Moreover, the method may comprise segmenting the trajectory into a macro level segment and further into a micro level segment. The method may further comprise computing at least one macro level score based on the plurality of acceleration samples and the GPS samples. Based on the at least one macro level score, the method may be configured to compute a crash responsibility score for ascertaining crash responsibility.