Remote Parking Assistant Error Classification for Safe Maneuvers
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Solution Overview
Problem
Existing remotely controlled parking systems lack robust error handling and communication fault management, which can lead to unsafe operations and driver confusion in case of errors or connection issues during parking maneuvers.
Innovation Solution
A method that reads and evaluates status data to assign states to classes, generating control and information records to handle error scenarios, differentiate between recoverable and non-recoverable faults, and inform the driver about errors to facilitate corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If remote-controlled parking is implemented without comprehensive error handling, then the system is simpler to operate, but safety and reliability are compromised
Solution Approach 1:
The system performs preliminary checks of status data before executing the parking maneuver. Error conditions are detected and evaluated in advance, allowing the system to prevent unsafe operations before they occur. This includes checking communication status, sensor functionality, and system readiness states prior to initiating remote parking.
Solution Approach 2:
The system continuously monitors status data during the parking maneuver and provides feedback about system state and error conditions. This feedback mechanism allows real-time detection of faults and enables the system to respond appropriately by aborting maneuvers or alerting the driver, thereby maintaining safety without requiring overly complex preventive measures.
2Difficulty of detecting and measuring
If comprehensive status monitoring is implemented, then error detection capability is improved, but the complexity of processing and responding to errors increases
Solution Approach 1:
Error handling is segmented into distinct state classes (e.g., recoverable errors, non-recoverable errors, warning states). Each state class has predetermined handling procedures, which simplifies the complexity by breaking down the error response into manageable segments rather than requiring complex decision logic for every possible error condition.
Solution Approach 2:
The system changes the parameter of error classification by categorizing errors into discrete state classes based on their severity and recoverability. This parameter change from continuous error analysis to discrete state classification simplifies the processing complexity while maintaining comprehensive error detection capability.
3Loss of information
If the driver is continuously informed about all error conditions, then awareness and ability to take corrective measures is improved, but information overload may occur
Solution Approach 1:
Information is provided with local quality by tailoring the type and amount of information to the specific error state and context. Critical errors receive prominent attention, while less severe conditions are communicated differently. This ensures the driver receives necessary information without being overwhelmed by unnecessary details.
Solution Approach 2:
The system converts potentially harmful error conditions into beneficial information by clearly communicating the nature and severity of errors to the driver. By framing errors as actionable information with clear state classifications, the system turns negative situations into opportunities for informed driver decision-making and corrective action.
Data Source
AI summary
The invention relates to a method for operating a motor vehicle (2) with a remotely controlled parking assistant (6), with the steps: (S100) Reading in status data (SD) indicative of a status (S1, S2, S3, S4), (S200) Determination of at least one state (Z1, Z2) by evaluating the status data (SD), (S300) Assigning the specific state (Z1, Z2) to a state class (K1, K2) of a plurality of state classes (K1, K2), and (S400) Outputting a control data record (AS1, AS2) that is assigned to the assigned state class (K1, K2) and/or (S600) outputting an information data record (IS1, IS2) that is assigned to the assigned state class (K1, K2).