Vehicular Collision Reconstruction for Fast, Reliable Fault Analysis
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Solution Overview
Problem
Conventional accident reconstruction methods are manual, unreliable, and time-consuming, often relying on biased and inconsistent information, which makes them slow and prone to inaccuracies in determining fault and damage assessment in vehicular accidents.
Innovation Solution
A system and method for accident reconstruction that utilizes a combination of machine learning and deep learning models to collect and analyze real-time data from mobile devices and vehicle sensors, providing a quantitative assessment of accidents and automating the reconstruction process, including collision detection, feature analysis, and output generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual methods are used for accident reconstruction, then human judgment and experience can be applied, but the process becomes slow and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis with automated electronic data processing. Telematics data from sensors, event data recorders, and mobile devices are processed by algorithms and machine learning models to automatically reconstruct accidents, replacing the manual mechanical process of gathering and analyzing information with an automated electronic system that provides both speed and accuracy
Solution Approach 2:
The system enables self-service accident reconstruction by automatically collecting data from multiple sources, processing it through analytical models, and generating reconstruction reports without requiring manual intervention. The automated system serves itself by gathering its own data, analyzing it, and producing results independently
2Adaptability or versatility
If manual data collection methods are used, then flexibility in investigation approaches is maintained, but information reliability and consistency deteriorate
Solution Approach 1:
The patent creates a universal system that can handle multiple types of accident data from various sources including telematics devices, event data recorders, mobile devices, and witness statements. The system processes different data types through a unified analytical framework, maintaining flexibility in data source selection while ensuring consistent processing and reliable results across all investigation scenarios
Solution Approach 2:
The system transforms qualitative human observations into quantitative measurable parameters by processing sensor data, acceleration patterns, and positional information through mathematical models. This parameter transformation ensures consistent, objective analysis while maintaining adaptability to different accident scenarios through configurable analytical thresholds and models
3Loss of information
If comprehensive data collection is performed manually, then complete accident information can be gathered, but the complexity and difficulty of data assessment increase
Solution Approach 1:
The patent segments the complex data collection and processing task into distinct functional modules: data acquisition from multiple sources, data validation and filtering, analytical processing through machine learning models, and result generation. Each module handles specific aspects of the reconstruction process independently, reducing overall system complexity while ensuring complete data collection and processing
Solution Approach 2:
The system introduces intermediary processing layers including data validation filters, normalization protocols, and machine learning models that act as mediators between raw data collection and final analysis. These intermediaries automatically manage the complexity of comprehensive data assessment by standardizing inputs and providing structured processing pathways
4Reliability
If conventional investigation processes are used, then human oversight can identify fraud and manipulation, but the process becomes slow and biased
Solution Approach 1:
The patent replaces manual fraud detection with automated analytical systems that process telematics data, sensor information, and event records through algorithms designed to identify patterns indicative of fraud or manipulation. This substitution maintains detection capability while dramatically increasing processing speed and eliminating human bias
Solution Approach 2:
The system implements feedback mechanisms where analytical results are continuously refined based on patterns identified in processed data. Machine learning models learn from accumulated cases to improve fraud detection accuracy over time, providing reliable detection that speeds up processing through automated iterative improvement rather than slow manual review
Data Source
AI summary
A system for accident reconstruction can include and/or be configured to interface with any or all of: a set of models, a set of modules, a processing system, client application, a user device (equivalently referred to herein as a mobile device), a set of sensors, a vehicle, and/or any other suitable components. A method for accident reconstruction includes collecting a set of inputs; detecting a collision and/or one or more features of the collision; reconstructing the collision; and producing an output based on the reconstruction. Additionally or alternatively, the method can include training a set of models and/or modules, and/or any other suitable processes.