Two-Wheeled Vehicle Accident Classification Using Multi-Sensor Variables
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Solution Overview
Problem
Existing systems struggle to accurately classify accident situations involving two-wheeled vehicles, particularly distinguishing between critical situations and accidents without direct collisions, such as a bicycle falling.
Innovation Solution
A method and device that utilize a combination of movement, collision, orientation, and impact variables acquired from sensors to generate classification variables, enabling the differentiation between various accident scenarios and severity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor variables are acquired and evaluated to classify accident events, then the accuracy of accident detection and classification is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The accident classification system is segmented into multiple independent sensor modules (acceleration sensors, rotation-rate sensors, tilt sensors, speed sensors) that each measure specific physical quantities. These segmented sensors work together to provide comprehensive accident detection while maintaining individual sensor simplicity and reducing overall system complexity.
Solution Approach 2:
The system transitions from single-dimensional accident detection to multi-dimensional classification by evaluating multiple sensor variables simultaneously (acceleration, rotation rate, tilt angle, speed) and their temporal characteristics. This dimensional expansion enables precise differentiation between various accident types (collisions, rollovers, falls) and severity levels.
2Reliability
If multiple sensor variables and their temporal characteristics are evaluated to distinguish critical situations from non-critical falls, then the reliability of accident classification is improved, but the loss of time for processing and analyzing data increases
Solution Approach 1:
The system continuously monitors and pre-processes sensor data during normal operation, maintaining a ready state for accident detection. By pre-establishing evaluation algorithms and classification criteria, the system can rapidly classify accidents when they occur without requiring extensive real-time computation, thus reducing processing time while maintaining high reliability.
Solution Approach 2:
The evaluation unit prioritizes critical temporal characteristics and key sensor variables during accident analysis, skipping less important processing steps. This selective rapid evaluation allows the system to quickly identify and classify critical accidents (such as severe collisions) while maintaining accuracy, reducing the time loss associated with comprehensive data analysis.
Data Source
AI summary
A method for classifying an accident event of a two-wheeled vehicle, in particular a bicycle. The method is able to run as an algorithm on a device having an evaluation unit in order to indicate to the driver of the two-wheeled vehicle or to a third party a collision or fall of the two-wheeled vehicle with the aid of correspondingly generated and/or transmitted information. The device may be used for a two-wheeled vehicle such as a bicycle and in particular for an electric bicycle. The use is naturally also possible for a motorcycle or some other single-track vehicle.