V2V Collision Detection via Server-Side Sensor Fusion
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Solution Overview
Problem
Current vehicle systems lack the capability to collect, analyze, and communicate driving data effectively across all vehicles, particularly in scenarios involving potential collisions, as not all vehicles are equipped with advanced sensors and communication systems.
Innovation Solution
A driving analysis computing device receives and analyzes positional data from multiple vehicles to determine if a collision has occurred, using real-time data transmission and analysis to calculate differences in X-axis, Y-axis, and Z-axis positional data, and transmits warnings based on historical driver behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle-based systems with sophisticated sensors are used to collect and analyze driving data, then measurement precision and reliability improve, but device complexity and cost increase significantly
Solution Approach 1:
The patent introduces a server as an intermediary that receives driving data from multiple vehicles, performs centralized analysis, and generates collision warnings. This mediator approach allows individual vehicles to use simpler sensor systems while achieving high measurement precision through server-side processing that aggregates and analyzes data from multiple sources including GPS, accelerometer, gyroscope, and other vehicle sensors.
Solution Approach 2:
The patent combines data from multiple vehicles and multiple sensor types (GPS receivers, accelerometers, gyroscopes, vehicle operation sensors) into a unified analysis system on the server. This merging of diverse data sources enhances measurement precision by cross-validating information and detecting collisions through multiple independent measurements, while individual vehicles maintain relatively simple onboard systems.
2Speed
If real-time data transmission and analysis is implemented across multiple vehicles, then collision detection speed improves, but communication bandwidth requirements and system complexity increase
Solution Approach 1:
The system continuously collects and pre-processes driving data from multiple vehicles before collisions occur, maintaining ready-to-analyze data streams from GPS, accelerometers, and other sensors. This preliminary action enables the server to immediately detect collisions by analyzing pre-captured data patterns, achieving high detection speed without requiring complex real-time communication protocols during critical moments.
Solution Approach 2:
The server implements feedback mechanisms by continuously monitoring driving data from multiple vehicles and providing collision warnings back to the involved vehicles. This feedback loop uses standardized communication protocols to transmit alerts when collisions are detected, enabling rapid response while maintaining manageable communication complexity through structured data exchange formats and threshold-based warning triggers.
3Measurement precision
If comprehensive vehicle sensor data is collected from all vehicles, then measurement precision improves, but loss of time for data processing increases
Solution Approach 1:
The server implements selective data processing by focusing analysis on specific collision-relevant parameters from the comprehensive sensor data, such as sudden acceleration patterns, GPS position changes, and gyroscope readings indicating impact. Rather than processing all available vehicle data equally, the system applies partial action by prioritizing analysis of data types most indicative of collisions, achieving high detection accuracy while reducing overall processing time through targeted computation.
Data Source
AI summary
One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and determine whether a collision is imminent or has occurred using vehicle-to-vehicle (V2V) communications. Determination of whether a collision has occurred may be based on X-axis, Y-axis, and Z-axis positional data from two vehicles. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles.