Virtual Testing of Autonomous Control Software Under Sensor Faults
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Solution Overview
Problem
Autonomous and semi-autonomous vehicles face challenges in ensuring safe operation, particularly in unusual environmental conditions or when sensors or components are damaged, as existing systems may not function properly and can pose risks to safety.
Innovation Solution
A computer-implemented method for monitoring and assessing autonomous vehicle control software and components, using simulated sensor data and an emulator program to evaluate the quality of autonomous operation features, determine risk levels, and implement remedial responses to malfunctions or damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous operation features are implemented in vehicles, then vehicle operation safety is improved, but the system may not function properly in unusual environmental conditions or when sensors are damaged, creating new safety risks
Solution Approach 1:
The system performs preliminary actions by continuously monitoring sensor functionality and environmental conditions before autonomous operation is compromised. The monitoring system detects malfunctions or unusual conditions in advance, allowing the vehicle to take preventive measures such as alerting the driver or switching to manual mode before safety is compromised.
Solution Approach 2:
The system implements feedback mechanisms where sensor data is continuously monitored and evaluated against expected performance parameters. When deviations indicating sensor malfunction or unusual environmental conditions are detected, the system provides feedback to adjust operation modes or alert operators, ensuring continuous safety assessment.
2Extent of automation
If existing autonomous control systems are used, then basic autonomous operation is achieved, but the systems pose risks when sensors or components are damaged
Solution Approach 1:
The monitoring system enables the autonomous vehicle system to self-assess its own operational status and sensor functionality. By continuously evaluating sensor data quality and system performance, the system can detect its own degradation or malfunction and take appropriate actions such as reducing automation level or alerting operators.
Solution Approach 2:
The system prepares compensatory measures in advance by having multiple operational modes ready (full autonomous, partial autonomous, manual override). When sensor damage or component failure is detected, the system can switch to a more conservative operational mode that compensates for the lost functionality, cushioning the impact on safety.
3Measurement precision
If continuous monitoring of sensor data is implemented, then detection of malfunctions is improved, but system complexity and computational requirements increase
Solution Approach 1:
The monitoring system extracts only the critical parameters and sensor data elements that are most indicative of malfunctions or unusual conditions. Rather than analyzing all sensor data equally, the system identifies and focuses on key indicators of system health and environmental anomalies, reducing computational burden while maintaining detection accuracy.
Data Source
AI summary
Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.