Vehicle Driving Limits via Neural Network Prediction
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Solution Overview
Problem
Current systems fail to accurately determine the rolling limits of vehicles on road traffic lanes, leading to reactive rather than anticipatory driving assistance, and existing navigation systems lack precision in providing driving limits, which can result in unsafe conditions, especially on complex road geometries and varying weather conditions.
Innovation Solution
A method and device using a neural network to determine rolling limits by combining geometric data of the road, vehicle characterization, and real-time trajectory data, incorporating data from various sources such as GPS, gyroscopes, and video images to provide real-time safety indicators and improve positioning, while also accounting for road surface conditions and vehicle characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reactive sensor-based systems (ABS, ESP) are used for vehicle behavior control, then real-time measurement capability is improved, but anticipatory capability deteriorates
Solution Approach 1:
The system performs preliminary action by calculating and predicting driving limits before the vehicle reaches critical states. Road geometry data, vehicle characteristics, and environmental conditions are pre-processed to determine anticipated driving limits, allowing the system to warn drivers before dangerous situations occur rather than reacting after sensors detect problems
2Ease of operation
If GPS positioning systems are used for navigation, then route guidance capability is improved, but positioning precision deteriorates
Solution Approach 1:
The system uses feedback by continuously comparing the vehicle's actual position (from GPS) with the expected position on the road network. Road geometry data serves as a reference framework, and deviations are detected and corrected, improving positioning accuracy while maintaining the ease of GPS-based navigation
3Adaptability or versatility
If road mapping systems are used for navigation reference, then route information availability is improved, but driving limit precision deteriorates
Solution Approach 1:
The system applies local quality by differentiating driving limits for specific local road conditions rather than using uniform road mapping data. Road geometry, surface characteristics, and environmental factors are analyzed to determine location-specific driving limits, providing precise local information while maintaining overall route guidance capability
4Adaptability or versatility
If multiple sensors and systems are integrated for comprehensive vehicle control, then system functionality is improved, but system complexity deteriorates
Solution Approach 1:
The system uses universality by creating a integrated platform that processes multiple data types (road geometry, vehicle characteristics, environmental conditions) through a unified calculation framework. This multi-functional approach consolidates various sensing and control functions into a single system that determines driving limits across different operating conditions, reducing overall system complexity
Data Source
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AI summary
The present invention relates to a method for setting the link between all types of vehicles and roadway and the limits for driving on said roadway. This link can be set on the basis of existing motoring databases and on the basis of known vehicle characteristics. This method can be used for determining the driving limits of a vehicle. The invention also relates to a device that can be installed on any vehicle whatsoever and is able to implement the method according to the invention. Application to the field of motoring.