Server-Based Driving Condition Prediction for Vehicle Efficiency
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Solution Overview
Problem
Existing driving condition prediction systems provide general and non-personalized information, failing to optimize vehicle efficiency and hazard risk based on specific vehicle characteristics and external conditions.
Innovation Solution
A method and system that use a server to receive vehicle characteristics and identify preceding vehicles' data to predict driving conditions for an approaching vehicle, utilizing machine-learning algorithms trained on datasets of various vehicles and road conditions, providing personalized and optimized driving instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general driving condition information is provided to all vehicles, then the system complexity is reduced and ease of operation is improved, but the driving efficiency and hazard risk optimization deteriorate because the information is not personalized to specific vehicle characteristics
Solution Approach 1:
The system pre-collects and stores driving condition data from multiple preceding vehicles before the current vehicle reaches the road segment. This preliminary data collection and processing enables personalized predictions when the vehicle approaches, resolving the contradiction by preparing personalized information in advance rather than generating it on-demand
Solution Approach 2:
The system creates a digital copy of driving conditions experienced by preceding vehicles with similar characteristics. By copying and adapting this data for the current vehicle's specific characteristics, the system provides personalized information without requiring complex real-time analysis, thus maintaining ease of operation while improving reliability
2Reliability
If personalized driving conditions are provided based on current and preceding vehicle characteristics, then the driving efficiency and hazard risk optimization are improved, but the device complexity and data processing requirements worsen
Solution Approach 1:
The server is designed to handle multiple functions: collecting data from numerous preceding vehicles, storing it in databases, identifying relevant preceding vehicles based on similarity, and generating personalized predictions. This multi-functional design consolidates complexity into a single centralized system rather than requiring complex client-side processing in each vehicle
Solution Approach 2:
The server acts as an intermediary between preceding vehicles and the current vehicle. It collects, processes, and stores driving condition data from multiple sources, then provides personalized predictions to the current vehicle. This intermediary approach simplifies the overall system architecture by centralizing data processing rather than requiring direct peer-to-peer communication between vehicles
3Measurement precision
If data from multiple preceding vehicles is collected and processed, then the accuracy of predicted driving conditions is improved, but the loss of time for data processing and the productivity worsen
Solution Approach 1:
The system performs preliminary data collection and storage during the time preceding vehicles actually traverse the road segment. By collecting and storing this data in advance in databases, the system avoids time-consuming processing when the current vehicle needs the prediction, thus resolving the contradiction between accuracy and time loss
Solution Approach 2:
The system collects data from multiple preceding vehicles (excessive action) but only processes and uses the data from the most relevant preceding vehicle identified through similarity comparison. This partial processing approach maintains high prediction accuracy while minimizing the actual data processing time required when the current vehicle needs the prediction
Data Source
AI summary
A method and system for providing a predicted driving condition to an electronic device associated with a current vehicle having a current vehicle characteristic. The method is executable on a server and comprises receiving an indication of the current vehicle approaching a road segment and the current vehicle characteristic; identifying a preceding vehicle which has a time of travel along the road segment before a current time, a time difference between the preceding vehicle time of travel and the current time being within a predetermined range, the preceding vehicle having a preceding vehicle characteristic; determining the predicted driving condition for the road segment, the predicted driving condition being based on the current vehicle characteristic and the preceding vehicle characteristic; and providing to the electronic device before the current vehicle reaches the road segment, the predicted driving condition for the current vehicle on the road segment.