Vehicle Flocking via Shared Virtual Environment for Traffic Optimization
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Solution Overview
Problem
Current systems for vehicle safety and traffic optimization are limited by individual vehicle operation, leading to increased safety risks and traffic congestion, as they rely on conservative driving and human vigilance, with proposed solutions like truck convoys and mapping applications either being risky or distracting and not effectively addressing these issues.
Innovation Solution
A system and method for vehicle flocking that creates a shared virtual environment using sensory data from autonomous vehicles, allowing vehicles to operate collectively by replicating a virtual environment across local computational devices, optimizing traffic flow through fluid dynamics or deep learning models, and assigning weights for preferential treatment of certain vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicles operate individually with conservative driving, then safety is maintained, but traffic congestion increases and travel time increases
Solution Approach 1:
The patent merges multiple vehicles into a coordinated flock that shares sensory data and operates collectively. Individual vehicles combine their sensors to create a shared environmental model, enabling the group to move more efficiently while maintaining safety through collective awareness rather than individual conservative behavior.
Solution Approach 2:
The system creates a virtual copy of the environment in each vehicle's computational device. This replicated virtual world allows vehicles to simulate and plan movements collectively without physical risk, enabling optimized traffic flow while maintaining safety through virtual rehearsal of maneuvers.
2Use of energy by moving object
If truck convoys are used to reduce drag and congestion, then fuel economy improves, but safety risk increases due to catastrophic collision potential
Solution Approach 1:
The vehicle flock dynamically adjusts spacing and positioning based on real-time sensory data and environmental conditions. Unlike rigid truck convoys with fixed spacing, the flock can adaptively change formation and distances, maintaining aerodynamic efficiency while preserving safety margins through continuous dynamic adjustment.
Solution Approach 2:
The shared virtual environment acts as an intermediary between vehicles in the flock. It provides a common reference frame for all vehicles to understand their relative positions and planned movements, enabling coordinated drafting behavior while maintaining safety through the virtual model that mediates physical interactions.
3Productivity
If mapping applications provide traffic information to drivers, then traffic relief may be achieved, but safety decreases due to driver distraction
Solution Approach 1:
The vehicle flock performs autonomous coordination and decision-making without requiring human driver intervention. The system self-manages traffic flow optimization through collective computational processing, eliminating the need for drivers to interact with distracting mapping applications while still achieving traffic relief through automated coordinated movement.
Solution Approach 2:
The system accelerates the decision-making process by performing collective environmental modeling and movement optimization in real-time. This rapid automated processing achieves traffic relief faster than human drivers can process mapping information, eliminating distraction while maintaining or improving safety through faster response times.
4Reliability
If redundant sensory systems are installed in each vehicle, then safety is improved through better collision avoidance, but cost increases significantly
Solution Approach 1:
The patent merges the sensory capabilities of multiple vehicles into a shared environmental model. Each vehicle contributes its sensor data to the collective, creating a more comprehensive view of the environment than any single vehicle could achieve alone. This shared sensing approach improves collision avoidance without requiring each vehicle to have expensive redundant systems.
Solution Approach 2:
The shared virtual environment serves multiple functions simultaneously: it provides collision avoidance, navigation planning, traffic flow optimization, and environmental awareness. This multi-functional system replaces the need for separate specialized systems in each vehicle, reducing overall cost while improving safety through comprehensive environmental understanding.
Data Source
AI summary
Systems and methods for generating a virtual environment in a flock of vehicles are provided. In this method a reflector is utilized to define a coverage area. Sensory data from autonomous vehicles within this coverage area is collected, along with non-vehicle data. Then a virtual environment may be replicated using the data at a local computational device on each of the vehicles via the transmission of messages through the reflector. Each vehicle can use this data to make decisions regarding movements, as well as having the traffic patterns optimized based upon an objective. When traffic flow is being optimized it is also possible to assign weights to the vehicles to provide them preferential treatment in the traffic flow model. The traffic flow model that is generated may be a fluid dynamics model, or may be based upon deep learning techniques. The objective for the model is generally to maximize total vehicle throughput in order to reduce overall traffic congestion.