Adaptive Vehicle Distance Sensor Signal Selection
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Solution Overview
Problem
Existing distance sensors for vehicles face challenges in reliably detecting objects in varying ambient conditions and interference, as they often rely on fixed transmission signals that are not optimized for different objects and environmental factors.
Innovation Solution
A method where successive measurement cycles are conducted using a computing device to classify objects based on received signals, selecting transmission signals from a plurality of predetermined signals using an association rule determined in a learning mode, allowing for adaptation to changing conditions and improved object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed transmission signals are used in distance sensors, then the device complexity is reduced and ease of operation is improved, but the reliability of object detection deteriorates in varying ambient conditions
Solution Approach 1:
The patent implements dynamic selection of transmission signals based on classified object types. The system transitions from fixed transmission signals to adaptive signal selection where the transmission signal characteristics (such as frequency, pulse duration, or modulation type) are dynamically adjusted according to the detected object class, thereby improving detection reliability without requiring manual intervention
Solution Approach 2:
The patent employs pre-determined association rules that are established during a learning mode before actual operation. These rules map object classes to optimal transmission signal configurations, allowing the system to quickly adapt to different objects without real-time complex analysis. The preliminary classification and rule-based selection reduce the computational burden during active detection cycles
2Adaptability or versatility
If transmission signals are varied based on ambient conditions, then the adaptability to different environments is improved, but the device complexity increases due to learning modes and association rules
Solution Approach 1:
The patent changes parameters of the transmission signal (such as frequency, amplitude, pulse width, or modulation scheme) based on the classified object type and ambient conditions. By adjusting these parameters dynamically, the system adapts to different detection scenarios including varying weather conditions, object materials, and distances, thereby improving environmental versatility
Solution Approach 2:
The system performs self-learning during a learning mode where it automatically establishes association rules between object classes and optimal transmission signals. This self-service capability allows the distance sensor to improve its own performance without external programming or manual configuration, reducing the need for complex pre-programming while enhancing adaptability
3Measurement precision
If multiple predetermined transmission signals are used for different object classes, then the measurement precision for different objects is improved, but the ease of operation deteriorates due to automated classification requirements
Solution Approach 1:
The patent implements a feedback mechanism where the received signals are continuously analyzed and classified into object classes. Based on this classification feedback, the system automatically selects the appropriate transmission signal from predetermined options. This closed-loop control ensures that the most suitable signal is used for each detected object type, maintaining high measurement precision while automating the process
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the reliability of object detection in the vehicle's surroundings by dynamically selecting transmission signals based on object classification, effectively handling changing ambient conditions and improving the identification of various objects, such as pedestrians, vehicles, and obstacles.
Implementation Method 1
a transmission signal is transmitted and the transmission signal reflected in an area surrounding the vehicle is taken as a basis for determining a received signal
Data Source
AI summary
The invention relates to a method for operating a distance sensor (4) of a vehicle (1), in which method a plurality of successive measurement cycles are carried out in an operating mode, wherein, in each measurement cycle, a transmission signal is transmitted, a reception signal (Rx1 to Rx8) is determined on the basis of the transmission signal reflected in a surrounding region (9) of the vehicle (1), the object (8) is classified, and the transmission signal is selected from a plurality of predefined transmission signals in accordance with how the object (8) is classified, wherein the transmission signal is selected in accordance with an assignment rule determined in a learning mode, said assignment rule describing an assignment of the plurality of predefined transmission signals to classes of objects (8), wherein, in each measurement cycle, the object (8) is classified on the basis of the reception signal (Rx1 to Rx8) and the transmission signal is selected in accordance with how the object (8) is classified for subsequent measurement cycles.