Vehicular Crash Reconstruction Using Mobile Sensor Scenario Modeling
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Solution Overview
Problem
Existing methods for reconstructing vehicular crashes rely heavily on human perception and memory, which can be unreliable and prone to errors, leading to inaccurate accident reports and delayed medical evaluations.
Innovation Solution
A network-based system and method that utilizes sensor data from mobile devices and vehicle computer systems to reconstruct vehicular crashes, generate scenario models, and determine the severity of crashes, thereby reducing reliance on human recollection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data collection systems are deployed in vehicles, then measurement precision and reliability of crash data is improved, but device complexity and cost increase
Solution Approach 1:
The patent integrates multiple sensors (accelerometers, gyroscopes, GPS) into a unified crash detection system that serves multiple functions: detecting crashes, determining severity, generating scenario models, and triggering emergency responses. This multi-functional approach improves measurement precision while managing system complexity through consolidation.
Solution Approach 2:
The system uses mobile devices as intermediaries between the vehicle's sensor data and the crash reconstruction process. The mobile device collects and transmits sensor data to remote servers for processing, reducing the complexity burden on the vehicle's onboard systems while maintaining high measurement precision.
2Reliability
If comprehensive sensor data is collected and processed, then reconstruction accuracy and injury detection are improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of sensor data by mobile devices before transmission to remote servers. Basic crash detection and initial scenario modeling are conducted locally, allowing for faster response time while comprehensive analysis is performed remotely with sufficient computational resources.
Solution Approach 2:
The crash reconstruction process is divided into multiple stages: immediate crash detection by mobile devices, initial scenario model generation, detailed analysis by remote servers, and iterative refinement. This segmentation allows critical functions to be performed quickly while comprehensive analysis follows, balancing speed and accuracy.
3Productivity
If automated scenario modeling is implemented, then productivity of crash reconstruction is improved, but device complexity and algorithm complexity increase
Solution Approach 1:
The system implements automated scenario modeling that reconstructs crash scenarios independently using sensor data without requiring manual intervention. The algorithm processes accelerometer, gyroscope, and GPS data to generate scenario models automatically, significantly improving productivity while the complexity is contained within the software rather than hardware.
Solution Approach 2:
The patent replaces manual crash reconstruction methods with automated computational algorithms. Instead of investigators manually analyzing sensor data, machine learning models and physics-based simulations automatically generate scenario models, improving efficiency while shifting complexity from human effort to software processing.
Data Source
AI summary
A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and/or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.