Crosswind Risk Determination via Relative Object Motion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in accurately determining real-time wind data for their location, which can lead to deviations from planned paths and increased energy expenditure due to insufficient specificity in wind data, even when receiving current weather data from remote servers.
Innovation Solution
A vehicle computer system that determines real-time wind data based on sensor data, including movement of objects relative to the vehicle, orientation, and weather conditions, and actuates vehicle components to compensate for crosswind risks, using machine learning algorithms and sensor fusion techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current weather data from remote servers is used, then general wind information is available, but the wind data lacks sufficient specificity for the vehicle's exact location
Solution Approach 1:
The system transitions from using general regional weather data to determining location-specific wind conditions by analyzing sensor data from objects in the immediate vicinity of the vehicle. This localizes the wind measurement to the exact position of the vehicle, resolving the contradiction between having general wind information and needing location-specific precision.
Solution Approach 2:
The patent replaces traditional mechanical weather stations or remote sensing systems with a vision-based system using cameras and machine learning algorithms. The system processes images of objects (such as trees, flags, or other reference objects) to infer wind speed and direction at the vehicle's specific location, substituting physical measurement infrastructure with computational analysis.
2Measurement precision
If real-time wind data is determined using sensor data and machine learning, then location-specific accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses a multi-functional vehicle computer that performs both standard autonomous driving functions and wind data determination. By leveraging existing sensors (cameras, GPS) and computational resources already present in autonomous vehicles, the system avoids adding dedicated specialized hardware, thus managing complexity while achieving precise real-time wind measurements.
Solution Approach 2:
The system determines wind data using the vehicle's own sensors and computational resources without requiring external infrastructure. The machine learning model processes images captured by the vehicle's cameras and automatically extracts wind information, making the system self-sufficient and avoiding the need for complex external measurement systems.
3Stability of the object's composition
If vehicle components are actuated to compensate for crosswind risk, then path stability is improved, but energy expenditure increases
Solution Approach 1:
The system determines crosswind risk in advance using visual sensors and machine learning before the wind significantly affects the vehicle's path. By predicting wind conditions and preparing compensation maneuvers beforehand, the system can make smaller, more efficient adjustments rather than reacting to large deviations, thus reducing overall energy expenditure while maintaining stability.
Solution Approach 2:
The system continuously monitors wind conditions using sensor data and adjusts vehicle control in real-time based on the determined crosswind risk. This closed-loop feedback allows the system to apply only the necessary compensation force to maintain path stability, avoiding excessive energy consumption that would result from over-compensation or continuous maximum correction.
Data Source
AI summary
Real-time wind data for a location is determined based on a detected movement of an object relative to a vehicle. The real-time wind data includes a wind speed and a wind direction. Upon receiving stored wind data for the location from a remote computer, a crosswind risk is determined based on the real-time wind data and the stored wind data. A vehicle component is actuated to compensate for the crosswind risk.