Automated Vehicle Coordination Using Shared Automation Capabilities
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Solution Overview
Problem
Automated vehicles (AVs) exhibit diverse driving behaviors due to varying automation levels, software versions, and features, complicating their operation and cooperation on shared roads.
Innovation Solution
AVs exchange wireless messages containing information on automation capabilities, manufacturer, software version, cooperation capabilities, and driving intentions to predict and adjust their driving behaviors, enabling coordinated operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AVs operate with different automation levels and software versions, then each vehicle can optimize its performance for specific scenarios, but the diversity of driving behaviors complicates prediction and cooperation between vehicles
Solution Approach 1:
The patent applies preliminary action by having AVs broadcast their automation capability information (automation level, software version, available features) before actual driving interactions occur. This allows other AVs to pre-adjust their prediction models and cooperative strategies based on the known capabilities of surrounding vehicles, thereby improving prediction reliability while maintaining automation diversity.
Solution Approach 2:
The patent implements feedback mechanisms where AVs continuously exchange information about their automation capabilities and observed driving behaviors. This feedback loop enables vehicles to refine their predictions of other AVs' behaviors by comparing predicted actions with actual observed actions, thereby improving the reliability of behavior prediction while preserving the benefits of diverse automation implementations.
2Measurement precision
If AVs exchange detailed information about automation capabilities, then behavior prediction accuracy improves, but communication data volume and processing complexity increase
Solution Approach 1:
The patent applies segmentation by dividing automation capability information into distinct, manageable parameters: automation level (SAE level), software version, and available features. This segmentation allows AVs to transmit and process information in structured, modular units, improving prediction accuracy through comprehensive data while reducing processing complexity through organized data structures.
Solution Approach 2:
The patent implements a universal information exchange framework where a single communication protocol handles multiple types of automation capability information (automation level, software version, features). This multi-functional approach allows diverse AV systems to exchange comprehensive information using a unified structure, improving prediction accuracy without proportionally increasing processing complexity.
3Reliability
If AVs continuously monitor and adjust their operation based on predicted behaviors of other vehicles, then safety and efficiency improve, but computational load and response time requirements increase
Solution Approach 1:
The patent applies local quality by having AVs focus their prediction and adjustment efforts on specific, locally relevant parameters such as relative position, velocity, and immediate driving intentions of nearby vehicles. Rather than continuously analyzing all possible automation capabilities of all surrounding vehicles, the system concentrates computational resources on the most critical local factors that directly impact safety and cooperative maneuvers.
Solution Approach 2:
The patent implements partial action by selectively adjusting only those aspects of AV operation that are most influenced by predicted behaviors of other vehicles, such as longitudinal speed and lateral positioning, while maintaining other operational parameters. This selective adjustment reduces computational load compared to continuously optimizing all vehicle parameters, while still achieving improved safety and efficiency through targeted cooperative control.
Data Source
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AI summary
The present invention relates to methods, apparatuses and computer programs for at least semi-autonomously operated vehicles, and in particular to adjustments being made to the at least semi-autonomous operation of said vehicles based on a prediction of a driving behavior of other at least semi-autonomously operated vehicles. A method for a first vehicle (100) is suitable for adjusting an at least semi-autonomous operation of the first vehicle based on a prediction of a driving behavior of one or more second vehicles (200), the one or more second vehicles being at least semi-autonomously operated vehicles. The method comprises receiving (110) one or more wireless messages from the one or more second vehicles, the one or more wireless messages comprising information on one or more automation capabilities the one or more second vehicles are capable of. The method comprises predicting (120) the driving behavior of the one or more second vehicles based on the information on the one or more automation capabilities the one or more second vehicles are capable of. The method comprises adjusting (130) the at least semi-autonomous operation of the first vehicle based on the prediction of the driving behavior of the one or more second vehicles.