Vehicle Hazard Detection via Cloud Extraction
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Solution Overview
Problem
Current digital maps lack the ability to effectively display sources of danger on routes, such as hazardous road conditions, as they do not have a method to determine and integrate such information efficiently.
Innovation Solution
A method and device that measure vehicle parameters while driving, compare them to thresholds, and form hazard data to identify potential dangers, including precise location assignment, using a combination of vehicle and environmental parameters, with the option to send this data to a cloud server for aggregation and warning generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hazard data is continuously collected and processed in the vehicle, then measurement precision and reliability of hazard detection are improved, but device complexity and computing resource consumption increase
Solution Approach 1:
The patent extracts the complex data processing function from the vehicle and relocates it to a cloud server. The vehicle only collects and transmits raw sensor data, while the cloud server performs the computationally intensive tasks of hazard detection, pattern recognition, and map generation. This extraction resolves the contradiction by maintaining high measurement precision through comprehensive data analysis while reducing device complexity in the vehicle.
Solution Approach 2:
The cloud server acts as an intermediary between the vehicle sensors and the hazard detection algorithms. Instead of directly processing all sensor data in the vehicle, the system uses the cloud server as a mediator to aggregate data from multiple vehicles, perform centralized analysis, and generate hazard information that is then transmitted back to vehicles. This intermediary approach distributes computational load and reduces vehicle complexity.
2Reliability
If hazard data is transmitted to a cloud server for aggregation, then reliability of hazard identification is improved through multiple data sources, but loss of time in data transmission and processing occurs
Solution Approach 1:
The cloud server continuously aggregates hazard data from multiple vehicles and pre-generates hazard information in advance, creating a proactive hazard database. When a vehicle approaches a known hazard location, the system can provide warnings before the vehicle actually encounters the danger, effectively using preliminary data preparation to reduce real-time processing delays while maintaining high reliability through aggregated data.
3Productivity
If vehicle parameters are measured and compared continuously, then productivity of hazard detection is improved, but use of energy increases
Solution Approach 1:
The patent extracts the continuous data processing function from the vehicle to the cloud server. The vehicle performs only lightweight tasks of collecting sensor data and transmitting it to the cloud, while the energy-intensive continuous analysis and hazard detection algorithms run on the cloud server. This extraction maintains high hazard detection productivity through continuous monitoring while dramatically reducing the vehicle's energy consumption for computing operations.
Data Source
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AI summary
The invention relates to a method for determining a source of danger on a travel route. According to the method, a vehicle parameter (101, 201) is measured during travel of a vehicle along the travel route and is compared to a vehicle parameter threshold value (103, 203). Danger data comprising the measured vehicle parameter and a vehicle position associated with the measurement are formed on the basis of the comparison in order to provide information that a source of danger is formed (105, 205) at the vehicle position associated with the measurement. The invention further relates to a corresponding device (401) and a corresponding computer program.