Autonomous Vehicle Mesh Networking for Trusted Collective Navigation
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Solution Overview
Problem
Autonomous vehicles in heterogeneous environments face limitations due to limited situational awareness and uncertainty about other vehicles' actions, leading to conservative behavior and suboptimal collective actions, as they rely primarily on on-board sensors and lack a reliable method to assess trustworthiness among vehicles.
Innovation Solution
A mesh network system where autonomous vehicles share information about location, status, and sensor data to collectively plan optimized motion paths and assess reliability, using a distributed ledger to build trust and optimize route planning, thereby reducing collision risks and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles rely primarily on on-board sensors to detect environment and plan routes, then each vehicle can operate independently with current technology, but situational awareness is limited and uncertainty about other vehicles' actions increases
Solution Approach 1:
The patent merges the sensing capabilities of multiple autonomous vehicles by having them share sensor data through a mesh network. Each vehicle's sensors detect environmental characteristics and vehicle actions, and this information is combined collectively to create a comprehensive situational awareness that no single vehicle could achieve alone.
Solution Approach 2:
The patent introduces an intermediary system (mesh network and data sharing protocol) that facilitates information exchange between autonomous vehicles. This intermediary enables vehicles to obtain information about other vehicles' actions and environmental characteristics without direct observation, reducing uncertainty.
2Reliability
If autonomous vehicles act conservatively to mitigate collision risks due to uncertainty, then safety is maintained, but travel speed and efficiency decrease
Solution Approach 1:
The patent implements feedback mechanisms where vehicles continuously share information about their actions and environmental detections. This feedback loop allows vehicles to update their understanding of the environment and other vehicles' intentions in real-time, enabling faster decision-making without compromising safety.
Solution Approach 2:
The patent enables preliminary action by having vehicles share their planned routes and intended actions with others in advance. This allows surrounding vehicles to anticipate and plan accordingly, reducing the need for conservative reactive behavior and enabling smoother, faster travel.
3Ease of operation
If each autonomous vehicle plans its own route independently based on self-interest, then individual vehicle autonomy is maintained, but collective actions become suboptimal and congestion increases
Solution Approach 1:
The patent merges individual route planning processes into a collective planning system. While each vehicle maintains its own autonomy, the vehicles combine their route plans and share information to identify and resolve conflicts, resulting in collectively optimized routes that improve overall traffic efficiency.
Solution Approach 2:
The system enables self-service by allowing vehicles to autonomously negotiate and adjust their routes based on shared information from other vehicles. Each vehicle independently makes decisions about route adjustments based on the collective data, maintaining autonomy while achieving collective optimization.
4Reliability
If autonomous vehicles give wide berths and travel slowly to account for uncertainty, then collision risk is reduced, but travel time increases
Solution Approach 1:
The patent applies preliminary action by having vehicles communicate their intended paths and actions to surrounding vehicles in advance. This allows other vehicles to plan their movements accordingly, reducing the need for large safety margins and enabling faster, more efficient travel while maintaining collision avoidance.
Data Source
AI summary
Disclosed herein are systems and methods for collectively optimizing cooperative actions among autonomous connected vehicles. A system may include a plurality of autonomous connected machines and/or vehicles that may establish a mesh network for communication amongst one another. Remote from the mesh network, the system may include a consortium configured to communicate with the mesh network, such as by cellular communication. The mesh network of connected vehicles may collectively receive, from the remote consortium, reliability information associated with the autonomous vehicles in the mesh network, and may collectively generate a shared map of the environment surrounding the plurality of autonomous connected vehicles in accordance with the reliability information. Based on the shared map, the mesh network of connected vehicles may collaboratively generate a collective navigation plan for the plurality of vehicles to navigate the environment.