Teleoperation AR Path Prediction for Communication Delay Compensation
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Solution Overview
Problem
Teleoperation systems experience inefficiencies due to time lags in communication links between vehicles and remote operators, leading to out-of-sync images that hinder effective vehicle control.
Innovation Solution
A system that estimates communication delays and generates augmented reality images by overlaying predicted vehicle paths, stopping distances, and target objects to provide remote operators with real-time situational awareness, compensating for time delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If teleoperation commands are transmitted over a wireless communication link, then remote vehicle operation is enabled, but time lag causes the displayed image to be out of sync with current vehicle reality
Solution Approach 1:
The system performs preliminary actions by predicting the vehicle's future position and generating augmented reality images that show where the vehicle will be, not just where it is. This includes calculating predicted vehicle paths based on current motion state and overlaying them on the display, so the operator sees future states in advance, compensating for communication time lag.
Solution Approach 2:
The system introduces an intermediary processing layer that acts as a mediator between the actual vehicle state and the displayed image. This intermediary generates augmented reality images that combine real vehicle data with predicted future states, creating an intermediate representation that bridges the time gap caused by communication delays.
2Loss of information
If real-time images are displayed on the operator terminal, then situational awareness is provided, but communication time delay causes the image to be out of sync with current vehicle reality
Solution Approach 1:
The system performs preliminary calculations to predict vehicle position, orientation, and path before displaying the image. By computing where the vehicle will be in the future based on current motion parameters, the system prepares augmented reality images that show future states, effectively compensating for the time delay in real-time display.
Solution Approach 2:
The system transitions from displaying only the current two-dimensional image to adding a temporal dimension by overlaying predicted future paths and positions. This creates a multi-dimensional display that includes past, present, and future vehicle states, allowing the operator to perceive time-related information spatially.
3Productivity
If the operator relies on delayed images for control decisions, then remote operation continues, but control accuracy deteriorates due to out-of-sync information
Solution Approach 1:
The system performs preliminary prediction of vehicle response to operator commands by calculating predicted vehicle paths based on current motion state and intended steering input. This allows the operator to see the anticipated result of their control actions before actually executing them, improving control precision despite time delays.
Solution Approach 2:
The system enhances feedback by providing predictive feedback that shows not just the current vehicle state but also the future state based on current motion trends and operator inputs. This enriched feedback loop allows the operator to make more accurate control decisions by understanding where the vehicle is heading, compensating for the delay in receiving actual state information.
Data Source
AI summary
A method to manage a vehicle is disclosed. The method may include obtaining vehicle inputs that may include an image captured by a vehicle camera. The method may further include estimating a time delay in teleoperation communication with the vehicle, and generating an augmented reality image based on the time delay and the vehicle inputs. The method may further include rendering the augmented reality image on a user interface to manage the vehicle.