Multi-Vehicle Group Visualization for Coordinated AR Operation
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Solution Overview
Problem
Existing systems for connected vehicles lack effective collaboration methods, particularly in automating tasks and visualizing relative positions, which limits their efficiency and safety when traveling in groups or performing common tasks.
Innovation Solution
A computer-implemented method and system that links multiple partially automated vehicles over a network, generates a 360-degree view from sensor data, aggregates this data into a group view, and displays it on augmented reality devices, while applying rules for coordinated operation, enabling enhanced collaboration and task completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple vehicles are linked into a group to perform common tasks, then collaboration efficiency and task completion capability are improved, but system complexity and coordination difficulty increase
Solution Approach 1:
The system segments the complex multi-vehicle collaboration problem into manageable components: individual vehicle 360-degree views, group view aggregation, and rule-based coordination. Each vehicle maintains its own sensor suite and processing capabilities while contributing to the collective group intelligence, allowing scalable complexity management.
Solution Approach 2:
A centralized server acts as an intermediary that receives data from multiple vehicles, aggregates individual 360-degree views into a comprehensive group view, and distributes coordinated control decisions back to vehicles. This mediator architecture simplifies peer-to-peer coordination complexity while enabling efficient group collaboration.
2Loss of information
If a 360-degree group view is generated and displayed on AR devices, then situational awareness and decision-making are improved, but data processing requirements and computational load increase
Solution Approach 1:
The system extracts only the essential spatial and contextual information needed for situational awareness from raw sensor data, displaying relative vehicle positions and group formation on AR devices without overwhelming computational processing of all raw sensor streams locally in each vehicle.
Solution Approach 2:
Instead of each vehicle processing and displaying complete 360-degree views from its own sensors, the system creates a virtual copy of the group environment by aggregating sensor data from all vehicles into a unified group view that is then distributed to each vehicle's AR device, reducing individual computational loads.
3Reliability
If rules are applied to coordinate vehicle operation within the group, then safety and task execution are improved, but control flexibility and adaptability decrease
Solution Approach 1:
The rule-based coordination system dynamically adjusts vehicle control parameters such as spacing, speed, and positioning within the group while maintaining safety constraints. Rules provide structured guidance that adapts to changing group configurations and task requirements, balancing safety enforcement with operational flexibility.
Data Source
AI summary
A computer-implemented method to provide collaboration between a group of vehicles. The method includes linking two or more vehicles into a group, wherein each vehicle is partially automated, includes a plurality of sensors, and are performing a common task. The method further includes generating, for each vehicle, a 360 degree view of the vehicle with data from the plurality of sensors. The method includes aggregating the 360 degree view for each vehicle into a group view, that includes all the grouped vehicles. The method includes displaying the group view on an augmented reality (AR) device a, where the group view includes a relative location for each vehicle in the group. The method includes applying a set of rules to the group of vehicles, wherein the rules provide instructions to each vehicle how to operate within the group, where the set of rules is configured to complete the task.