Teleoperator Predicted View for Autonomous Vehicle Latency
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Solution Overview
Problem
Autonomous vehicles face challenges when encountering unfamiliar or complex scenarios, as they may struggle to determine how to traverse them with certainty. Additionally, latency and delays in presenting information to remote operators can hinder their ability to provide assistance effectively.
Innovation Solution
The implementation of a system that generates predicted views of an environment for remote operators, using a combination of diffusion models and variable autoencoders. This system processes sensor data and occupancy information to create a predicted view, which can be displayed to remote operators even when real-time data is delayed or unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If real-time sensor data is transmitted to remote operators, then operators can see the current situation, but network latency and instability cause delays in presenting this information
Solution Approach 1:
The system performs preliminary actions by generating predicted views of future environmental states before the actual sensor data arrives. The machine learning model predicts what the environment will look like at future time points based on current and historical data, allowing operators to see anticipated situations in advance rather than waiting for delayed real-time data.
Solution Approach 2:
The system creates copies of the environmental view through generated imagery that mimics what sensor data would show. Instead of directly transmitting raw sensor data that may be delayed, the system generates visual copies representing predicted environmental states, providing operators with timely visual information that replicates the essential situational details.
2Reliability
If the system waits for real sensor data before presenting information to operators, then data accuracy is maintained, but operators experience delays in becoming aware of situations
Solution Approach 1:
The system performs preliminary predictions of environmental states before actual sensor data arrives. By generating predicted views in advance based on current data and machine learning models, the system ensures operators receive timely information without waiting for potentially delayed real-time data transmissions, thus maintaining both reliability and productivity.
3Loss of time
If the system generates predicted views using machine learning models, then operators receive timely information, but system complexity increases
Solution Approach 1:
The system introduces an intermediary machine learning model that acts as a mediator between raw sensor data and operator display. This intermediary component generates predicted views by processing current sensor data and historical information, bridging the gap between data collection and information presentation while managing the complexity through specialized prediction algorithms.
4Loss of information
If real-time data transmission is used, then operators see current vehicle environment, but network instability causes data loss and delays
Solution Approach 1:
The system performs preliminary generation of predicted environmental views using local machine learning models before relying on network transmission. By predicting environmental states locally using current and historical sensor data, the system reduces dependence on stable network connections for basic situational awareness, maintaining information completeness even when network reliability is poor.
Data Source
AI summary
A remote operation system may provide, to remote operators of autonomous vehicles that have requested assistance traversing an environment, predicted views of the environment to account for latency in networking and/or computing that may cause an original view to be stale by the time it's presented at a remote operator's device. The remote operation system may initialize a connection to an autonomous vehicle for remote operator assistance, the request including sensor data associated with the autonomous vehicle, the sensor data including an image. The remote operation system may then generate a predicted image based on the received image of the sensor data of the vehicle and display the predicted view to a remote operator.