Vehicle Sensor Self-Diagnostics via Mutual Observation
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Solution Overview
Problem
Current vehicle sensor systems, particularly in autonomous vehicles, require extensive diagnostic efforts to ensure proper operation, which can be time-consuming and resource-intensive, especially in fleets where continuous monitoring is necessary.
Innovation Solution
A method where two vehicle sensor systems observe each other through homogeneous transformation to detect system features and compare relative information, allowing for self-diagnosis without additional effort, using existing sensors and communication units to identify any errors and facilitate targeted maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional diagnostic methods are used for vehicle sensor systems, then diagnostic accuracy can be maintained, but diagnostic effort and time consumption increase significantly
Solution Approach 1:
The vehicle sensor system performs self-diagnosis by autonomously comparing its own sensor data with reference values and identifying deviations without requiring external diagnostic equipment or manual intervention. The control unit automatically processes sensor signals, compares them against stored reference data, and generates diagnostic results, enabling the system to diagnose itself during normal operation.
Solution Approach 2:
The system implements continuous feedback by constantly comparing real-time sensor measurements with reference values stored in memory. The control unit receives sensor signals, compares them against reference data, and uses the comparison results to identify deviations or errors in sensor functionality, creating a closed-loop diagnostic process that continuously monitors system health.
2Reliability
If extensive diagnostic procedures are implemented, then system reliability improves, but device complexity and maintenance effort increase
Solution Approach 1:
The existing sensor system components are made multi-functional by enabling them to serve both their primary measurement function and a secondary diagnostic function. The same sensors that measure environmental parameters are also used to diagnose system health by comparing their outputs against reference values, eliminating the need for separate dedicated diagnostic sensors or equipment.
Solution Approach 2:
The control unit performs diagnostic evaluation autonomously using existing system resources. It compares sensor signals with reference values stored in its memory, identifies deviations automatically, and generates diagnostic results without requiring external diagnostic tools or complex additional hardware, thereby maintaining simplicity while ensuring reliable diagnosis.
3Reliability
If continuous monitoring is performed in vehicle fleets, then operational reliability is maintained, but resource consumption and maintenance needs increase
Solution Approach 1:
The diagnostic process operates continuously during normal vehicle operation without interrupting or adding to the vehicle's primary functions. The control unit continuously compares sensor signals with reference values in real-time, enabling ongoing monitoring of sensor health throughout the vehicle's operational lifecycle without requiring separate monitoring sessions or stopping vehicle operation.
Solution Approach 2:
The system uses its own operational data and existing computational resources to perform continuous self-diagnosis. The control unit leverages the vehicle's normal operating sensors and processing capabilities to monitor its own health, avoiding the need for additional external monitoring equipment or increased energy consumption from dedicated diagnostic systems.
Data Source
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AI summary
In order to be able to carry out self-diagnostics on a vehicle sensor system (FSESA), in particular on an autonomous vehicle in a simple manner and without large additional outlay, it is proposed that in the course of the self-diagnostics of the vehicle sensor system (FSESA) this vehicle sensor system (FSESA) and a further vehicle sensor system (FSESB) observe one another and in the process acquire, in a sensor-system-based fashion (SEA, SEB) in each case at least one extracted system feature (FSESA_SM_FSESB, FSESA_SM`_FSESB, FSESB_SM_FSESA, FSESB_SM`_FSESA) and generate, as a function of sensor-system-related, extrinsic and intrinsic system parameters (SPAe, SPAi, SPBe, SPBi) and the acquired system features "6D-Systempose [FSESA_Th_FSESB], [ FSESA_Th_FSESB] *, [FSESB_Th_FSESA], [FSESB_Th_FSESA] *"-based relative information items (RI1, RI1*, RI2, RI2*) independently of one another by means of homogenous transformation [Th], which relative information items (RI1, RI1*, RI2, RI2*) are preferably formed by the respective vehicle sensor system by means of the entire system parameters/system features or a subset thereof. After this, the vehicle sensor system FSESA compares the self-generated relative information items (RI2, RI2*) of the relative information items (RI2, RI2*) which were generated and transferred by the further vehicle sensor system FSESB. When identity is detected (in the case of identically observed information), the vehicle sensor system (FSESA) operates normally within the scope of the observed system parameters/system features, and otherwise a fault is present.