Autonomous Vehicle Perception Feedback for Sensor Error Adaptation
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining accurate object detection and navigation due to intrinsic sensor errors, environmental conditions, and calibration issues, which can lead to performance degradation and safety risks.
Innovation Solution
A system that compares actual detection parameters of objects to baseline detection parameters, adjusting vehicle behavior and triggering operations to enhance perception system performance by using classification groups and multi-dimensional inference models to account for various environmental factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use sensor data for object detection and navigation, then the vehicle can safely navigate autonomously, but sensor errors and environmental conditions cause performance degradation and detection inaccuracies
Solution Approach 1:
The system continuously monitors detection performance by comparing actual detection parameters (detection distance, detection time, localization accuracy, classification confidence) against baseline parameters. This feedback loop enables real-time identification of performance degradation and triggers appropriate responses such as cleaning or recalibration operations to maintain detection accuracy despite sensor errors and environmental conditions
Solution Approach 2:
The perception system performs self-diagnosis and self-correction by automatically detecting performance deviations and triggering cleaning or recalibration operations without external intervention. The system monitors its own detection performance and initiates corrective actions to maintain reliability, enabling autonomous vehicles to compensate for sensor errors and environmental factors through self-service maintenance
2Reliability
If the vehicle adjusts control strategy based on detection performance comparison, then navigation safety is improved, but system complexity increases due to continuous monitoring and adjustment mechanisms
Solution Approach 1:
The system implements a feedback mechanism where detection performance parameters are continuously monitored and compared against baselines, triggering control strategy adjustments only when performance deviations exceed thresholds. This feedback-based approach maintains navigation safety by responding to actual performance needs rather than requiring complex continuous control, balancing reliability improvement with acceptable system complexity
3Reliability
If the system performs real-time comparison of detection parameters to baseline parameters, then performance degradation is detected earlier, but processing time and computational load increase
Solution Approach 1:
The system performs partial monitoring by comparing only key detection parameters (detection distance, detection time, localization accuracy, classification confidence) against baselines rather than analyzing all possible sensor data. This selective parameter comparison enables timely performance degradation detection while minimizing processing time and computational load by focusing on the most critical performance indicators
Data Source
AI summary
Example embodiments relate to self-supervisory and automatic response techniques and systems. A computing system may use sensor data from an autonomous vehicle sensor to detect an object in the environment of the vehicle as the vehicle navigates a path. The computing system may then determine a detection distance between the object and the sensor responsive to detecting the object. The computing system may then perform a comparison between the detection distance and a baseline detection distance that depends on one or more prior detections of given objects that are in the same classification group as the object. The computing system may then adjust a control strategy for the vehicle based on the comparison.