Vehicle Safety Threshold Control Using Dynamic Object Map Data
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Solution Overview
Problem
Existing emergency brake assist systems rely solely on ego-vehicle perception and static map data, which can lead to inadequate preparation for collision risks, especially in conditions of poor visibility, such as on winding country roads.
Innovation Solution
Utilizing real-time map data from other vehicles to dynamically determine the predefined distance to dynamic objects, adjusting the activation threshold of safety systems based on object type and proximity, and incorporating wireless data transmission to update map data for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If emergency brake assist systems rely solely on ego-vehicle perception and static map data, then the system complexity is reduced, but the reliability of collision risk detection deteriorates in poor visibility conditions
Solution Approach 1:
The patent combines multiple data sources including real-time map data from other vehicles, ego-vehicle sensor data, and dynamic object tracking to create a comprehensive collision risk assessment system. This merging of information sources improves reliability without excessively increasing system complexity through integrated processing architecture.
Solution Approach 2:
The system performs preliminary actions by receiving and processing real-time map data from other vehicles before collision risks materialize. By proactively identifying dynamic objects and preparing safety systems in advance, the system improves collision detection reliability while maintaining manageable complexity through staged processing.
2Measurement precision
If real-time map data from multiple vehicles is integrated to improve collision detection accuracy, then measurement precision of dynamic object positions increases, but device complexity increases
Solution Approach 1:
The patent segments the data processing task by dividing it into distinct modules: receiving map data from other vehicles, processing ego-vehicle sensor data, tracking dynamic objects, and generating collision risk assessments. This segmentation improves measurement precision through specialized processing while controlling device complexity through modular architecture.
3Measurement precision
If the predefined distance is dynamically adjusted based on object type to improve detection accuracy, then measurement precision of risk assessment increases, but device complexity increases
Solution Approach 1:
The system changes the predefined distance parameter dynamically based on object type and risk assessment. By adjusting this key parameter according to the specific situation rather than using a fixed value, the system achieves high measurement precision in risk assessment while managing complexity through parameter-based control rather than structural complexity.
Data Source
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AI summary
There is presented a method for controlling an activation threshold of a safety system of a vehicle. The method comprises receiving map data from a remote data repository, said map data comprising a geographical location of a dynamic object located in a surrounding area of an expected path of the vehicle, determining a geographical location of the vehicle by means of a localization system of the vehicle, and lowering an activation threshold value of the safety system when the geographical location of the vehicle is within a predefined distance from the dynamic object. The presented method provides for an efficient means for preparing e.g. an emergency brake assist system of a vehicle in potentially critical situations.