Feature-Flagged Vehicle Routing Graphs for Real-Time Route Constraints
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Solution Overview
Problem
Existing routing systems for autonomous vehicles face inefficiencies in managing diverse roadway conditions, vehicle capabilities, and business policies, leading to costly and cumbersome modifications of routing graphs, which can be inefficient and impractical for real-time changes.
Innovation Solution
Implementing a general-purpose routing graph with routing graph modification data that can be applied dynamically to generate a constrained routing graph, allowing for real-time adjustments based on vehicle capabilities, roadway conditions, and business policies, without the need for constant updates or new graph creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If routing graphs are modified to accommodate diverse roadway conditions, vehicle capabilities, and business policies, then routing accuracy and adaptability improve, but system complexity and computational overhead increase
Solution Approach 1:
The routing system is segmented into a base routing graph and multiple independent modification layers. Each modification layer handles specific aspects (roadway conditions, vehicle capabilities, business policies) separately, allowing the system to maintain a simple core structure while achieving high adaptability through compositional modifications.
Solution Approach 2:
The base routing graph is designed as a universal structure that can serve multiple purposes. Through application of modification layers, the same base graph adapts to different roadway conditions, vehicle types, and policy requirements, eliminating the need for separate routing graphs for each scenario.
2Reliability
If routing graphs are updated frequently to reflect real-time changes, then routing accuracy improves, but data storage and management costs increase
Solution Approach 1:
The system extracts only the necessary modification data from complete routing graph recreations. Instead of storing and processing entire updated routing graphs, only the differential modification layers are stored and applied, significantly reducing data storage requirements while maintaining routing accuracy.
Solution Approach 2:
Modification layers are prepared and validated in advance before being applied to the base routing graph. This preliminary action ensures routing accuracy is maintained while avoiding the need for frequent complete graph recreations, reducing both storage requirements and computational overhead.
3Manufacturing precision
If multiple routing graphs are created for different vehicle types and conditions, then routing precision for specific vehicles improves, but system complexity and data management become cumbersome
Solution Approach 1:
A single base routing graph serves all vehicle types and conditions universally. Vehicle-specific routing precision is achieved not through multiple graphs but through application of appropriate modification layers that encode vehicle capabilities, roadway conditions, and policy constraints.
Solution Approach 2:
Instead of creating globally different routing graphs for different vehicles, the system applies localized modifications to the base graph through modification layers. Each layer introduces vehicle-specific or condition-specific constraints only where necessary, maintaining routing precision without multiplying the entire graph structure.
Data Source
AI summary
Systems and methods herein describe accessing a set of feature flags, a first feature flag in the set of feature flag describing vehicle navigation behavior on a routing graph, the routing graph representing the roadway, associating the set of feature flags with a vehicle, applying the associated set of feature flags to a graph traversal algorithm to generate a modified graph traversal algorithm, generating the route using the modified graph traversal algorithm; and transmitting instructions to the vehicle to begin executing the route.