Autonomous Vehicle Scene Reliability for Stale Sensor Data
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Solution Overview
Problem
Autonomous vehicles face challenges in determining the reliability of sensor data, particularly video data, when connectivity is poor, leading to outdated representations of the environment, which can compromise safety and navigation accuracy.
Innovation Solution
A system that determines a reliability metric for sensor data based on parameters such as time difference, vehicle speed, distance traveled, and environmental factors, and adjusts the display output accordingly to indicate data reliability, ensuring operators are aware of data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If video data is transmitted from autonomous vehicle to remote system over network, then operators can monitor vehicle environment, but data becomes outdated when connectivity is poor
Solution Approach 1:
The system pre-calculates and stores reliability metrics alongside video frames before transmission. This preliminary action ensures that when data is transmitted, the reliability information is already prepared and can be immediately used to assess data freshness without requiring real-time computation during transmission or playback.
Solution Approach 2:
The system implements feedback by continuously monitoring network conditions, vehicle movement, and environmental factors to dynamically update reliability metrics. This feedback mechanism allows the system to adjust data transmission and display strategies based on current conditions, ensuring operators are always aware of data freshness and reliability status.
2Reliability
If reliability metric system is implemented, then operator awareness of data accuracy is improved, but system complexity increases
Solution Approach 1:
The reliability metric system is segmented into distinct functional modules: network condition monitoring, vehicle state tracking, environmental factor analysis, and metric calculation. Each module operates independently and contributes to the overall reliability assessment, making the complex system more manageable and easier to implement without requiring complete system redesign.
3Measurement precision
If multiple parameters are monitored for reliability calculation, then data accuracy assessment is improved, but processing requirements increase
Solution Approach 1:
The system applies local quality by weighting different parameters differently based on their current relevance and reliability contribution. Not all parameters are processed with equal computational resources at all times - the system dynamically adjusts processing intensity for each parameter based on local conditions, optimizing energy usage while maintaining measurement precision.
Data Source
AI summary
A computer implemented method is provided. The method, comprises: receiving from an autonomous vehicle sensor data, wherein the sensor data is indicative of an environment in which the vehicle is currently located or was previously located. The method further comprises receiving or determining additional data associated with at least one of: the sensor data, the vehicle, or the environment, wherein the additional data is different from the sensor data. The method further comprises displaying, on a display, an output comprising a representation of the sensor data. The method further comprises determining, based at least in part on the additional data, a reliability metric, the reliability metric being indicative of how reliable the sensor data is at representing the environment in which the vehicle is located at a current time. The method further comprises causing the output on the display to be based at least in part on the reliability metric.