Vehicle Forecast Fusion for Real-Time Road Condition Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing weather and road condition forecasting systems rely on unreliable, locally based information from weather stations and lack quick and accurate updates, leading to significant uncertainty and inefficiency in road network management and vehicle operations.
Innovation Solution
A computer-based system in vehicles that combines remote weather and road condition data with onboard sensor data using a fusion process, including Kalman filtering and smoothing algorithms, to provide real-time, optimized forecasts without continuous online connection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weather forecasts are provided by remote institutions using fixed weather stations, then coverage area is extensive, but measurement precision and reliability deteriorate due to local-based information and update delays
Solution Approach 1:
The patent combines remote forecast data from weather stations with local sensor measurements from vehicles to create an enriched forecast. This merging of data sources allows the system to maintain the broad coverage of remote institutions while adding local precision through vehicle-based sensors that detect actual road conditions, weather parameters, and vehicle operation data in real-time.
Solution Approach 2:
The system applies local quality by using vehicle-mounted sensors to capture hyper-local environmental conditions and road surface states that differ from broader weather station readings. Each vehicle becomes a localized measurement point, providing granular data about specific road sections that improves forecast accuracy for local conditions while maintaining connection to remote forecast frameworks.
2Productivity
If weather forecasts are updated every hour by remote institutions, then system complexity is reduced, but productivity and responsiveness worsen due to slow update frequency
Solution Approach 1:
The system enables vehicles to self-update their local forecasts continuously using onboard sensors without requiring external server requests for each update. Vehicles autonomously collect and process weather and road condition data, generating real-time forecasts independently while occasionally syncing with remote institutions to maintain alignment with broader weather patterns.
Solution Approach 2:
The system implements feedback loops where vehicle sensor data continuously updates and refines local forecasts in real-time. This feedback mechanism allows the system to respond immediately to changing conditions on road surfaces and in the environment, dramatically increasing update frequency from hourly to near-real-time while using algorithms that manage computational complexity.
3Measurement precision
If continuous online connection is required for forecast updates, then measurement precision improves through real-time data, but ease of operation worsens due to connectivity requirements
Solution Approach 1:
The system performs preliminary actions by continuously collecting and processing weather and road condition data locally using vehicle-mounted sensors. This preliminary data collection and processing enables the vehicle to maintain accurate local forecasts independently, allowing operation without continuous online connectivity while preserving real-time forecast accuracy through onboard computational capabilities.
Data Source
AI summary
The present disclosure relates to a method for operating a computer-based system (10) in a vehicle (2), for determining an updated and optimized forecast relating to at least a weather or a road condition of a road section (3) in a network (1) of roads, said method comprising the following steps: providing a first set of data (6) from a remote provider (4) of information related to at least a weather or a road condition in at least said road section (3), said first set of data (6) corresponding to said forecast; transmitting said first set of data (6) to said vehicle (2); and providing a second set of data (9) based on at least the operation of the vehicle (2) or present conditions in the surroundings of the vehicle (2). Furthermore, said method comprises the steps of: combining, in said computer-based system (10), said first set of data (6) with said second set of data (9) for obtaining said updated and optimized forecast; and providing the updated and optimized forecast to a user. The disclosure also relates to a computer-based system (10) in a vehicle (2), for determining said updated and optimized forecast.