Coordinated Multi-Vehicle Routing for Autonomous Fleet Efficiency
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Solution Overview
Problem
Autonomous vehicles (AVs) face challenges in improving energy efficiency and safety when operating individually, as they do not effectively share computational tasks or coordinate navigation, leading to increased energy consumption and interactions with other vehicles.
Innovation Solution
A fleet management system that identifies clustering opportunities and facilitates the formation and dissolution of AV clusters/platoons, allowing for the distribution of computational tasks and improved route coordination using predictive analytics and edge vehicle designations based on sensor capabilities and traffic dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles operate individually without coordination, then each vehicle maintains full computational autonomy and navigation independence, but energy consumption increases and safety decreases due to lack of task sharing and cluster coordination
Solution Approach 1:
The patent merges multiple autonomous vehicles into coordinated clusters or platoons, allowing them to share computational tasks and sensory processing. By combining vehicles into groups that travel together with coordinated navigation, the system reduces individual energy consumption while maintaining or improving overall safety through distributed intelligence and shared situational awareness.
2Use of energy by moving object
If autonomous vehicles form clusters for task distribution and route coordination, then energy efficiency improves through computational task sharing and aerodynamic benefits, but system complexity increases due to coordination requirements
Solution Approach 1:
The patent segments the autonomous vehicle system into hierarchical layers: individual vehicle control systems maintain basic autonomy, while a higher-level fleet management system handles cluster formation and coordination. This segmentation allows energy-efficient task distribution and route coordination without requiring every vehicle to process all coordination decisions, thereby reducing overall system complexity while maintaining energy efficiency benefits.
3Use of energy by moving object
If autonomous vehicles coordinate navigation and share computational tasks in clusters, then energy demands are reduced through distribution, but the difficulty of detecting and measuring coordination effectiveness increases
Solution Approach 1:
The patent implements feedback mechanisms where the fleet management system continuously monitors cluster performance metrics including energy consumption, task completion efficiency, and coordination quality. This feedback enables the system to measure coordination effectiveness objectively and adjust cluster configurations, task assignments, and routing decisions to optimize energy efficiency while maintaining measurable performance standards.
4Use of energy by moving object
If autonomous vehicles travel in closely spaced clusters, then aerodynamic performance improves reducing energy consumption, but interactions with non-cluster vehicles increase potential safety risks
Solution Approach 1:
The patent introduces a fleet management system as an intermediary that coordinates cluster movements and manages interactions with non-cluster vehicles. This intermediary monitors the surrounding environment, predicts potential conflicts, and adjusts cluster positioning or speed to minimize dangerous interactions while preserving aerodynamic efficiency benefits. The mediator handles external communications and conflict resolution, allowing closely spaced clusters to operate safely among other traffic.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enhances the energy efficiency and safety of AVs by reducing energy demands through task distribution, improving aerodynamics, and minimizing interactions with non-cluster vehicles, while maintaining smooth user experiences.
Implementation Method 1
utilize predictive analytics, for example, to predict overlapping AV routes based on historic fleet navigation patterns
Implementation Method 2
When multiple vehicles share overlapping routes, the formation of closely spaced vehicle clusters (also: platoon or train) can improve fuel economy
Data Source
AI summary
The disclosed technology provides solutions for improving the operational efficiency and safety of autonomous vehicles (AVs). In some implementations, the disclosed technology encompasses methods for performing vehicle clustering or platooning, for example, that can include steps for computing navigation routes for each of a plurality of autonomous vehicles (AVs), identifying a route overlap for a first AV and a second AV selected from among the plurality of AVs, and transmitting a clustering instruction to the first AV and the second AV based on the route overlap, wherein the clustering instruction is configured to cause the first AV and the second AV to initiate a clustering configuration. Systems and machine-readable media are also provided.