Autonomous Vehicle Perception Feedback for Sensor Detection Drift
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Solution Overview
Problem
Autonomous vehicles face challenges in maintaining accurate object detection and navigation due to intrinsic sensor errors, calibration issues, weather conditions, and other environmental factors, which can lead to performance degradation and safety risks.
Innovation Solution
A system that compares actual detection parameters of objects with baseline detection parameters specific to classification groups, allowing real-time adjustments to vehicle behavior and sensor operations to enhance performance and safety, using multi-dimensional inference models for comprehensive environmental analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles use sensor data for object detection, then navigation capability is improved, but detection accuracy deteriorates due to sensor errors and environmental factors
Solution Approach 1:
The system continuously compares actual detection parameters (detection distance, detection angle, detection confidence) against baseline parameters stored in memory, and uses this feedback to adjust vehicle control strategies in real-time, thereby compensating for sensor errors and environmental interference
Solution Approach 2:
The system dynamically changes detection parameters by comparing actual measurements with baseline values and adjusting control strategies based on the differences, allowing the vehicle to adapt to varying environmental conditions and maintain detection accuracy
2Reliability
If the vehicle adjusts control strategy based on detection comparisons, then safety is improved, but system complexity increases
Solution Approach 1:
The system divides the control strategy into multiple discrete levels (first control strategy, second control strategy, third control strategy) based on detection confidence thresholds, allowing complex safety responses to be managed through modular, threshold-based decision-making
Solution Approach 2:
The system performs self-diagnosis and self-adjustment by automatically comparing its own detection parameters against baselines and modifying its control strategy without external intervention, thereby improving safety while maintaining autonomous operation
3Reliability
If baseline detection parameters are established for classification groups, then detection reliability is improved, but processing time increases
Solution Approach 1:
The system pre-establishes baseline detection parameters for different object classification groups before actual detection occurs, allowing rapid comparison and decision-making during real-time operation without requiring complex processing of raw sensor data
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.