Vehicle Impact Sensor Network for Faster Severity Detection
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Solution Overview
Problem
Existing vehicle impact detection systems face delays in processing and communicating the severity of impacts due to the large number of interactions between the main control unit and individual sensors, which can hinder timely emergency responses.
Innovation Solution
A system where impact sensors communicate with each other to determine their displacement and severity, using a library of pre-impact locations to quickly assess displacement and automatically notify a remote facility if the displacement exceeds a threshold, thereby reducing processing time and facilitating rapid emergency response.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the main control unit interacts with each sensor individually to determine impact severity, then measurement precision is improved, but processing time increases
Solution Approach 1:
The system divides the sensor network into multiple groups, with each group independently calculating impact severity metrics for its local area. This segmentation allows parallel processing of impact data from different vehicle zones, reducing overall processing time while maintaining comprehensive measurement precision through aggregated group results.
Solution Approach 2:
Impact sensors continuously monitor and pre-process baseline data during normal vehicle operation, maintaining ready-to-use impact thresholds and calibration parameters. When an impact event occurs, the pre-prepared data structures and algorithms enable immediate severity assessment without requiring time-consuming initial setup or individual sensor queries.
2Measurement precision
If more sensors are deployed on the vehicle, then impact severity measurement accuracy is improved, but device complexity increases
Solution Approach 1:
The extensive sensor network is organized into autonomous groups that each manage their own subset of sensors. Each group independently processes data from its member sensors, reducing the communication overhead between the main control unit and individual sensors. This hierarchical structure maintains high measurement precision through comprehensive sensor coverage while reducing system complexity through decentralized processing.
Solution Approach 2:
Multiple sensors within each group are merged into a single processing unit that aggregates their readings to determine local impact severity. This merging reduces the number of individual sensor-data interactions required while preserving the combined measurement capability of all sensors in the group, thereby reducing device complexity without sacrificing measurement accuracy.
3Productivity
If individual sensor interactions are reduced to decrease processing time, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments the impact assessment task into multiple parallel group-level evaluations rather than sequential individual sensor queries. Each sensor group simultaneously processes its local sensors' data and produces an impact severity metric, enabling parallel computation that increases productivity while maintaining precision through the aggregation of all group results.
Solution Approach 2:
Each sensor group maintains a local copy of the impact assessment algorithms and threshold parameters needed for severity determination. This copying eliminates the need for the main control unit to individually query each sensor or retrieve processing instructions, thereby increasing productivity through localized autonomous processing while preserving measurement precision through consistent algorithm application across all groups.
Data Source
Figure 1
Figure 2A~2D
Figure 2E~2F
AI summary
A method and system for detecting a vehicle impact and automatically requesting emergency assistance is provided. The system comprises multiple impact sensors located on the vehicle. The impact sensors are configured to communicate with each other as well as with a main control unit.