Autonomous Vehicle Sensor Management via Baseline Comparison
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Solution Overview
Problem
Computer-assisted or autonomous driving vehicles face challenges in maintaining reliable sensor performance due to factors like weather conditions, dirt, and normal sensor degradation, which can affect the accuracy of sensor data and require distinct management approaches.
Innovation Solution
A system that continuously monitors sensor conditions by comparing baseline readings with subsequent readings from landmarks, using a sensor management apparatus to determine the cause of degradation and notify users of appropriate corrective actions, such as cleaning or servicing, through integration with a remote database system for differential experience sharing among vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor monitoring is performed continuously to detect degradation, then sensor reliability is improved, but system complexity increases due to baseline comparison and differential experience analysis
Solution Approach 1:
The system performs preliminary actions by establishing baseline readings for each sensor before degradation occurs. These baselines are stored and used for future comparison, allowing the system to detect degradation proactively rather than reactively. The baseline serves as a reference point that enables continuous monitoring without requiring complex real-time analysis of every sensor state change.
Solution Approach 2:
The patent introduces an intermediary mechanism - the differential experience system - that mediates between individual sensor readings and degradation detection. By comparing a vehicle's sensor readings against aggregated data from other vehicles with similar sensors, the system can distinguish between normal variations and actual degradation, reducing false positives and simplifying the monitoring logic.
2Measurement precision
If baseline readings are recorded and compared to detect sensor degradation, then measurement precision is improved, but loss of time occurs due to continuous monitoring and data comparison
Solution Approach 1:
The system implements periodic action by establishing baselines at specific intervals or under specific conditions rather than continuously. Baseline readings are taken when sensors first detect landmarks or at predetermined time intervals, and comparisons are made periodically. This approach maintains measurement precision while reducing the continuous time burden of monitoring.
Solution Approach 2:
The patent applies partial action by not requiring continuous full-scale analysis of all sensor data. Instead, the system performs targeted comparisons only when relevant - such as when landmarks are detected or when degradation thresholds are approached. This selective monitoring maintains detection accuracy while minimizing time consumption.
3Adaptability or versatility
If differential experiences from other vehicles are analyzed to determine management actions, then adaptability is improved, but device complexity increases due to remote database integration
Solution Approach 1:
The patent applies universality by creating a multi-functional system where the remote database serves multiple purposes: storing baseline data, aggregating differential experiences from multiple vehicles, providing comparison references, and enabling fleet-wide pattern recognition. This single infrastructure supports diverse functions that would otherwise require separate systems, reducing overall complexity while enhancing adaptability.
Solution Approach 2:
The system uses copying by creating virtual representations of sensor experiences across the fleet. Instead of physically analyzing each vehicle's sensor data in real-time, the system copies and stores differential experience data from multiple vehicles, then uses these copies for comparison and pattern recognition. This approach enables adaptive decision-making without requiring complex real-time communication with all vehicles.
Data Source
AI summary
An apparatus for sensor management in computer-assisted or autonomous driving (CA/AD) vehicles, comprising a sensor analyzer and a sensor manager. The sensor analyzer is to determine a difference between a baseline sensor reading of a landmark, and a subsequent reading of the same landmark at a later point in time. The sensor manager is to determine whether a management action is needed on the basis of the determined difference, in further consideration of differential experiences from other CA/AD vehicles.