Autonomous Vehicle Data Freshness Checks for Safe-State Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in ensuring safe operation due to inconsistencies in receiving critical data, which can lead to increased processing time, decreased efficiency in identifying obstacles, and unsafe operation.
Innovation Solution
The implementation of a communication framework within autonomous vehicles that allows systems to subscribe to critical data topics, monitor data availability and freshness, and validate data integrity, enabling the vehicle to transition to a safe state when critical data events occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the vehicle increases processing time to better understand data, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system performs preliminary actions by validating data integrity and checking data freshness before full processing. The communication framework verifies data quality attributes (integrity, freshness, completeness) upfront, allowing the system to quickly determine whether detailed processing is necessary, thus improving efficiency without sacrificing accuracy when data is valid
2Productivity
If the vehicle uses received data for operation, then productivity is improved, but reliability deteriorates due to potential unsafe operation
Solution Approach 1:
The system performs preliminary validation of data integrity and freshness before using the data for vehicle operations. The communication framework checks data quality attributes upfront, ensuring that only validated data is used for control decisions, thus maintaining reliability while enabling continuous operation
Solution Approach 2:
The communication framework acts as an intermediary layer between data sources and vehicle control systems. It validates data quality attributes and filters out unsafe or stale data before it reaches the control systems, enabling productivity while guaranteeing reliability through this mediating validation layer
3Reliability
If the vehicle implements comprehensive data validation, then reliability is improved, but device complexity increases
Solution Approach 1:
The validation framework is segmented into distinct, modular checks for different data quality attributes (integrity, freshness, completeness). Each attribute is validated independently through separate communication framework components, making the overall system more manageable and less complex than a monolithic validation approach
Data Source
AI summary
Autonomous vehicles rely on many types of information for operation. Such information may include safety critical information, which may be information essential to safe operation of the system. In an example, a vehicle system may identify events associated with critical information. Events may include failing to receive critical information as expected or determining that received critical information is unsuitable, for example, because it is too old, invalid, or otherwise lacking integrity. In some examples, based at least in part on determining the event, the vehicle may be controlled in a safe state.