Autonomous Vehicle Routing Graphs Using Temporal Roadway Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in incorporating temporal data into routing systems, as common sources of temporal data do not directly map to route components, and assigning costs to route components experiencing high traffic or weather conditions is nontrivial, affecting the predictability and efficiency of vehicle routing.
Innovation Solution
The use of temporal data items that describe roadway conditions and locations, correlated to route components of an autonomous vehicle routing graph, to generate a constrained routing graph, which modifies costs and connectivity, allowing for real-time adjustments to route planning based on current conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temporal data is incorporated into routing systems, then routing predictability and efficiency are improved, but device complexity and difficulty of implementation increase
Solution Approach 1:
The patent introduces temporal data items as intermediaries that bridge the gap between raw temporal data and routing decisions. These data items serve as a standardized interface that simplifies the integration of complex temporal information into the routing system, making the system more predictable without proportionally increasing complexity
Solution Approach 2:
The system dynamically changes routing parameters based on temporal data. By adjusting route costs and connectivity parameters in response to temporal conditions (traffic, weather, construction), the system achieves better predictability and efficiency while managing complexity through parameter-based control
2Productivity
If temporal data is used to adjust routing in real-time, then travel efficiency is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system applies partial action by selectively adjusting routing parameters only for route components affected by temporal conditions. Instead of processing and adjusting all routing parameters globally, the system focuses computational resources on specific affected segments, improving travel efficiency while managing data processing complexity
Solution Approach 2:
The system performs preliminary correlation of temporal data items with route components before routing decisions are made. By pre-processing and organizing temporal data in advance, the system reduces real-time processing requirements and enables more efficient route calculations
3Measurement precision
If common sources of temporal data are integrated, then routing accuracy is improved, but mapping difficulties and implementation challenges increase
Solution Approach 1:
The patent creates a universal temporal data item structure that can accommodate multiple sources of temporal data (traffic, weather, construction). This multi-functional data structure enables the system to integrate diverse data sources with a single unified approach, improving routing accuracy while reducing the complexity of mapping different data sources to route components
Data Source
AI summary
Various examples are directed to systems and methods for routing an autonomous vehicle. For example, a system may access temporal data comprising a first temporal data item. The first temporal data item may describe a first roadway condition, a first time, and a first location. The system may also access a routing graph that comprises a plurality of route components and determine that a first route component of the routing graph corresponds to the first location. The system may generate a constrained routing graph at least in part by modifying the first route component based at least in part on the first roadway condition. The system may additionally generate a route for an autonomous vehicle using the constrained routing graph; and cause the autonomous vehicle to begin traversing the route.