Autonomous Vehicle Routing via Dynamic Geographical Areas
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle routing systems fail to efficiently account for temporary and permanent constraints, such as road closures and high-traffic areas, leading to suboptimal route planning and deployment of autonomous vehicle fleets.
Innovation Solution
A computer-implemented method that determines routing constraints, including inclusion thresholds based on path frequency of use, to define geographical areas where autonomous vehicles can safely and efficiently navigate between source and destination locations, avoiding excluded zones and optimizing route paths in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If routing systems use static route planning without considering dynamic constraints, then route planning is simple and fast, but route efficiency and safety deteriorate due to inability to account for temporary road closures and high-traffic areas
Solution Approach 1:
The system dynamically adjusts routing constraints by continuously monitoring real-time data from multiple sources including traffic conditions, road closures, and weather. Constraints are updated in real-time rather than remaining static, allowing the routing system to adapt to changing conditions and maintain both efficiency and safety
Solution Approach 2:
The system implements feedback loops where routing decisions are continuously evaluated against actual vehicle performance and constraint compliance. Data from autonomous vehicles about actual route conditions feeds back into the system to refine and update constraints, improving future route planning while ensuring ongoing safety and compliance
2Reliability
If routing systems implement comprehensive constraint monitoring and dynamic geographical area definition, then route safety and compliance improve, but system complexity increases
Solution Approach 1:
The system segments the operational space into defined geographical areas (waffles) with specific constraint characteristics. Each area is evaluated and defined independently based on its own set of constraints, allowing complex routing problems to be broken down into manageable segments that can be processed more efficiently
Solution Approach 2:
The system creates a multi-functional constraint management framework that handles multiple types of constraints (road closures, traffic conditions, weather, vehicle-specific requirements) through a unified geographical area definition approach. This universal system manages diverse constraints through a single coherent mechanism rather than separate systems for each constraint type
3Object-affected harmful factors
If routing paths avoid high-traffic areas and excluded zones, then vehicle safety improves, but deployment efficiency and fleet utilization deteriorate
Solution Approach 1:
The system dynamically adjusts routing constraints by continuously monitoring real-time data from multiple sources including traffic conditions, road closures, and weather. Constraints are updated in real-time rather than remaining static, allowing the routing system to adapt to changing conditions and maintain both efficiency and safety
Solution Approach 2:
The system changes routing parameters dynamically based on real-time conditions. When conditions improve in previously excluded zones (such as high-traffic areas during off-peak hours), the system adjusts parameters to allow route usage, thereby improving fleet utilization while maintaining safety standards
Data Source
AI summary
According to one aspect, routing of an autonomous or semi-autonomous vehicle may be based at least in part upon the availability of particular geographical areas through which the vehicle may be routed. A geographical area may be defined, based on constraints such as road conditions, lane features, operational constraints, etc., such that at least one routing path between each of one or more sources and one or more destinations within the geographical area may be traversed by a vehicle. Constraints may be temporary and may be accounted for substantially in real-time when defining and/or updating a geographical area within which a vehicle is to operate. In some instances, an inclusion constraint can be defined based on a frequency of use of a given routing path associated with one or more points of interest for a geographical area. Accounting for constraints enables autonomous vehicles to be deployed safely and efficiently.