Perception Error Modeling for Autonomous Vehicle Safety Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for simulating autonomous vehicle safety testing are inefficient and fail to accurately model realistic perception errors, particularly those caused by sensor data quality and computational constraints, making it impractical to achieve the required safety level of one error per 10^7 decisions.
Innovation Solution
The use of Perception Statistical Performance Models (PSPMs) to model probabilistic uncertainty distributions based on actual perception outputs, allowing for the simulation of realistic perception errors and outputs, which are then used to generate statistically valid scenarios for testing autonomous vehicle systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If actual driven test miles are used for safety testing, then realistic driving scenarios can be tested, but the number of test miles required becomes prohibitively large to achieve human-level safety standards
Solution Approach 1:
The patent creates virtual copies of real-world driving scenarios by recording actual sensor data, planner decisions, and environment states during real tests, then replaying these scenarios in simulation with modified parameters. This copying approach allows extensive testing without requiring proportional increases in real-world test miles
Solution Approach 2:
The system performs preliminary data collection during real-world testing by recording comprehensive sensor outputs, environment states, and decision outcomes. This pre-recorded data is then reused multiple times in simulation with various modifications, eliminating the need to repeatedly conduct identical real-world tests
2Reliability
If photorealistic simulation is used to model perception errors, then realistic sensor data can be generated, but the computational complexity and data quality requirements become excessively high
Solution Approach 1:
The patent extracts only the essential elements needed for perception error modeling from complex photorealistic simulation - specifically, it uses simple geometric models of sensors and environments combined with statistically-derived error parameters. This extraction eliminates unnecessary visual fidelity while retaining the core functionality of modeling perception uncertainties
Solution Approach 2:
The system changes the approach from modeling perception through complex visual rendering to modeling it through parameter-based uncertainty distributions. By using statistical parameters derived from real perception system performance rather than attempting to visually simulate perception processes, the system achieves realistic error modeling with much lower complexity
3Adaptability or versatility
If the perception stack is modified to improve functionality, then system capabilities are enhanced, but the number of test miles required increases to validate the changes
Solution Approach 1:
When the perception stack is modified, the system copies existing test scenarios and reuses them with the updated perception algorithms. This allows rapid validation of changes without requiring new real-world test campaigns, as the virtual scenarios can be efficiently re-executed with modified code
Solution Approach 2:
The test scenario database serves multiple functions: it validates original system performance, tests modified perception stacks, identifies edge cases, and provides baseline comparisons. This multi-use approach maximizes the value of each recorded scenario and reduces the need for additional testing
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Herein, a "perception statistical performance model" (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled. The modelled perception slice includes an online error estimator, and the computer system is configured to use the PSPM to obtain a predicted online error estimate for the perception output in response to the perception ground truth. This recognizes that online perception error estimates may, themselves, be subject to error.