Sensor Reliability Evaluation for Autonomous Driving Perception
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Solution Overview
Problem
Current sensing systems for autonomous vehicles lack a unified solution for reliable perception and communication in complex environments, with varying sensing technologies needing fusion and advanced algorithms to handle vast data sets while ensuring safety and security against adversarial attacks.
Innovation Solution
A method and system for evaluating the influence of external actions on sensor data by determining a signal reliability factor, which assesses coherence with expected values and adjusts sensor configurations, using statistical rules and Bayesian methods to prioritize and manage sensor data for accurate vehicle control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor fusion and advanced algorithms are used to handle vast data sets, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The patent segments the sensor system into multiple independent sensor units, each capable of autonomous evaluation of external actions. This segmentation allows complex sensing tasks to be divided into manageable components while maintaining high measurement precision through distributed processing and statistical evaluation methods.
Solution Approach 2:
The patent introduces an intermediary evaluation layer that processes sensor data before final interpretation. This intermediary system uses statistical rules and Bayesian methods to evaluate the influence of external actions, acting as a mediator between raw sensor data and control decisions, thereby managing complexity while preserving precision.
2Reliability
If signal reliability factor evaluation is performed, then reliability is improved, but loss of time increases due to additional processing
Solution Approach 1:
The patent performs preliminary evaluation of sensor data coherence with expected values before full processing. By pre-assessing signal reliability factors and filtering out obviously incoherent data early in the processing pipeline, the system establishes reliability without requiring extensive processing time for all data points.
Solution Approach 2:
The patent changes the parameter of evaluation from comprehensive analysis to targeted statistical assessment. By using signal reliability factors based on statistical rules and Bayesian probability rather than full data analysis, the system achieves reliable evaluation with reduced processing time through parameter optimization.
3Measurement precision
If sensor configurations are adjusted to manage data priority, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic sensor configuration adjustment based on evaluated data priority and signal reliability. Sensor configurations are not fixed but adapt in real-time according to environmental conditions and data quality assessments, allowing the system to optimize measurement precision while using automated rules to manage configuration complexity.
Data Source
AI summary
The present disclosure provides a non-transitory computer readable medium having instructions tangibly stored thereon, wherein when executed by a processing entity, the instructions cause the processing entity to carry out a method of evaluating influence of an action performed by an external entity, the method comprising: receiving sensor data; determining a signal reliability factor for the received sensor data, wherein the signal reliability factor represents a statistical quality indication between the received sensor data and an expected value or an expected range of values; and associating the signal reliability factor with the received sensor data. There is also provided a system configured to communicate with an autonomous driving system.