Petri Net Reachability Graph for Sensor Malfunction Detection
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Solution Overview
Problem
Current methods for sensor systems, such as those used in autonomous driving, fail to adequately assess the operating states covered by validation data sets and cannot determine if new data sets may cause malfunctions, leading to potential system failures.
Innovation Solution
A method is developed to generate a reachability graph using a Petri net that models sensor system tasks and their conditions, allowing for the estimation of all possible operating states and recognition of potential malfunctions by processing validation data sets through this graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing algorithms and hardware-in-the-loop simulation are used for sensor system validation, then object recognition capability is improved, but the ability to assess coverage of operating states and detect potential malfunctions deteriorates
Solution Approach 1:
The patent creates a virtual copy of the sensor system's software architecture using a Petri net model. This abstract representation copies the essential structure and behavior of the original system, allowing analysis of all possible operating states without requiring extensive physical validation data. The Petri net captures task dependencies, resource constraints, and execution flows, enabling comprehensive malfunction detection while maintaining focus on object recognition capabilities.
Solution Approach 2:
The patent performs preliminary analysis by generating a reachability graph that maps all possible operating states before actual sensor system deployment or validation. This advance modeling identifies potential malfunctions and coverage gaps in advance, allowing the system to be prepared with knowledge of critical states that need monitoring, thereby improving both object recognition validation and malfunction detection capability.
2Reliability
If validation data sets are used to verify sensor system behavior, then functional correctness is improved, but the ability to determine coverage of internal operating states deteriorates
Solution Approach 1:
The patent implements feedback by comparing the actual operating states observed during validation against the complete state space defined in the reachability graph. This feedback mechanism identifies which operating states have been covered by validation data and which remain unexplored, providing quantitative information about coverage gaps. The system uses this feedback to determine whether additional validation scenarios are needed to achieve comprehensive coverage.
Solution Approach 2:
The Petri net model serves as an intermediary between the validation data sets and the sensor system's internal operating states. Rather than directly observing complex internal states, the model translates validation inputs into reachable state information, mediating the connection between external validation and internal system behavior. This intermediary provides structured information about state coverage without requiring direct access to proprietary sensor internals.
3Strength
If stress testing with expert estimates is used to evaluate sensor system stability, then system robustness is improved, but the ability to systematically identify all possible operating states deteriorates
Solution Approach 1:
The patent uses dynamic analysis of the Petri net model to systematically explore all possible operating states. Rather than relying on static expert estimates, the reachability graph dynamically computes which states are actually reachable given the system's task dependencies and resource constraints. This dynamic approach efficiently identifies the complete state space without requiring exhaustive stress testing of every conceivable scenario.
Solution Approach 2:
The patent changes the analysis parameters from expert-estimated stress scenarios to mathematically-derived reachability states. By transforming the problem from physical stress testing to formal model analysis, the system efficiently explores all possible operating states through computational enumeration rather than gradual parameter adjustment during stress testing. This parameter transformation dramatically improves state exploration efficiency while maintaining robustness assessment.
Data Source
AI summary
A method for generating a reachability graph for recognizing a malfunction of a sensor system. The sensor system is operable with software configured to execute a number of tasks in a certain order. The method includes: generating a Petri net containing information about the tasks of the software of the sensor system and their order, each task being described by a relevant transition of the Petri net, which is executed by the software if required condition(s) for that task are fulfilled; initializing the Petri net by an initial marking, by corresponding conditions for executing at least one or more tasks of the software being set as fulfilled; propagating along the Petri net starting from the initial marking by determining tasks of the software for which the required conditions are fulfilled at each relevant propagation step; generating a reachability graph with nodes, which include information about fulfilled required conditions.