Remote Driving Handover Timing Using Traffic and pQoS Prediction
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Solution Overview
Problem
Current systems lack an efficient method for safely and economically switching a transportation vehicle from automated driving mode to remote driving mode, particularly in situations where rapid resource consumption and discomfort occur, and fail to determine optimal handover points for remote driver control.
Innovation Solution
A method that determines driving preferences of remote drivers, predicts future traffic situations, and assesses the quality of service of communication links to decide on switching from automated to remote driving mode, optimizing resource usage and comfort by identifying convenient and safe intervals for handover.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If switching from automated driving mode to remote driving mode is performed rapidly, then response time is improved, but resource consumption increases and driver comfort deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future traffic situations and identifying optimal handover points before the actual mode switching occurs. The prediction unit forecasts traffic scenarios, and the determination unit identifies convenient intervals for handover, allowing the system to prepare for mode switching in advance rather than reacting abruptly, thus reducing resource consumption while maintaining timely response.
Solution Approach 2:
The system dynamically adjusts the timing and conditions of mode switching based on real-time traffic situation predictions and communication quality assessments. Instead of fixed or rapid switching, the system adapts the handover process to optimal moments identified through continuous prediction and evaluation, balancing response time with resource efficiency.
2Loss of time
If switching from automated driving mode to remote driving mode is performed rapidly, then response time is improved, but driver comfort deteriorates
Solution Approach 1:
The system determines driving preferences and predicts optimal handover points in advance, allowing smooth transitions at convenient intervals rather than abrupt switches. This preliminary planning ensures that mode changes occur at moments most suitable for driver comfort while still maintaining adequate response time.
Solution Approach 2:
The system incorporates feedback mechanisms by assessing communication quality and comparing predicted traffic situations against driver preferences. This feedback loop allows the system to adjust handover timing to optimize both response time and driver comfort, avoiding uncomfortable rapid transitions while maintaining operational efficiency.
3Device complexity
If mode switching is performed without optimization, then operational simplicity is maintained, but resource consumption increases
Solution Approach 1:
The prediction unit and determination unit work together to identify optimal handover points in advance, avoiding unnecessary or premature mode switching. This preliminary analysis reduces the frequency of mode changes and associated resource consumption while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The system changes operational parameters by introducing prediction time horizons, communication quality thresholds, and driver preference weights. These parameter adjustments enable optimized resource usage through intelligent handover timing without significantly increasing operational complexity, as the optimization is handled automatically by the control system.
4Device complexity
If mode switching is performed without optimization, then operational simplicity is maintained, but economic efficiency deteriorates
Solution Approach 1:
By predicting future traffic situations and identifying optimal handover points in advance, the system avoids unnecessary mode switching that would consume resources without providing benefit. This preliminary optimization improves economic efficiency by ensuring that each mode change serves a genuine operational need, while the automated nature of the process maintains operational simplicity.
Solution Approach 2:
The system uses feedback from communication quality assessment and traffic situation prediction to make economically efficient handover decisions. By continuously evaluating whether conditions warrant mode switching, the system improves productivity and resource utilization without requiring complex manual intervention, maintaining operational simplicity while enhancing economic efficiency.
Data Source
AI summary
A transportation vehicle, an infrastructure component, an apparatus, a computer program, and a method for a transportation vehicle to be remotely operated by a remote driver in a remote driving mode and to be operated at least partially automatically in an automated driving mode. The method includes determining driving preferences of a remote driver from the driving behavior of the remote driver, predicting information on a future traffic situation for switching from the automated driving mode to the remote driving mode, determining a predicted quality of service (pQoS) of a communication link for the remote driving mode, predicting a remote operation interval for which the transportation vehicle is at least operable in the remote driving mode based on the pQoS and the information on the future traffic situation, and deciding for or against a change from the automated driving mode to the remote driving mode.