Autonomous Vehicle Route Coordination for Conflict-Aware Traffic Flow
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Solution Overview
Problem
Current autonomous transportation networks face scalability issues and traffic congestion due to the lack of efficient management of multi-featured autonomous vehicles, particularly during peak hours, as existing systems are not designed to handle vehicles of different dimensions and dynamics, leading to potential route conflicts and infrastructure overload.
Innovation Solution
A system and method that utilize a control management center to calculate and manage permissible routes for multi-featured autonomous vehicles, considering their dimensions and dynamics, predict potential conflicts, and generate conflict avoidance instructions using traffic patterns and deep-learning algorithms, allowing for real-time adjustments to prevent congestion and optimize route usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central control management system is used to control autonomous vehicles, then routing coordination is improved, but system complexity increases
Solution Approach 1:
The patent divides the control system into two segments: a central control management that calculates permissible routes and sends them to vehicles, and onboard vehicle systems that independently calculate the same routes using identical algorithms. This segmentation allows centralized coordination while distributing computational complexity, reducing the burden on any single component.
Solution Approach 2:
The patent implements identical route calculation algorithms in both the central control management and onboard vehicle systems. This universal approach ensures that both systems arrive at the same routing decisions independently, providing redundancy and consistency without requiring complex communication protocols for real-time negotiation.
2Adaptability or versatility
If the ATN maps each origin to all destinations, then route coverage is improved, but the system becomes non-scalable as the matrix expands
Solution Approach 1:
The patent pre-calculates and stores permissible routes for all origin-destination pairs in a database during system setup. This preliminary action allows the system to handle any origin-destination combination without performing complex real-time calculations, enabling full route coverage while maintaining scalability as new routes can be added by simply updating the database.
3Quantity of substance
If different types and sizes of autonomous vehicles are used, then capacity on highly frequented routes is improved, but handling complexity increases
Solution Approach 1:
The patent applies different permissible route sets to different vehicle types based on their specific characteristics. The central control management determines which routes are permissible for each vehicle type considering factors like vehicle dimensions, weight, and capacity. This local quality approach allows the system to optimize capacity for each vehicle type while managing complexity through automated route restriction rules.
Data Source
AI summary
A system for operation of an autonomous transportation network and a method of operation for a plurality of multi-featured autonomous vehicles are disclosed. The system comprises a road, a control management center and a plurality of multi-featured autonomous vehicles. The multi-featured autonomous vehicles include different types of vehicles for transportation of passengers or goods in the autonomous transportation network. The method of operation disclosed comprises selecting permissible routes from an origin to a destination for the multi-featured autonomous vehicles and predicting conflicts for the multi-featured autonomous vehicles. Conflict avoidance instructions are generated and are transmitted to the multi-featured autonomous vehicles using infrastructure elements. The method of operation comprises adjusting the route of the multi-featured autonomous vehicles (20) using corrected route instructions calculated by an onboard processor of the multi-featured autonomous vehicles.