Autonomous Vehicle Docking Location Partitioning
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently associating passenger docking locations with destinations within vehicle transportation networks, particularly in determining optimal docking points based on pedestrian travel time and network partitioning.
Innovation Solution
An autonomous vehicle system that identifies and associates passenger docking locations with destinations using vehicle transportation network partitioning, where a processor determines target docking locations by analyzing pedestrian travel times and road segments, and a trajectory controller navigates the vehicle to these locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the autonomous vehicle system uses comprehensive vehicle transportation network partitioning and pedestrian travel time analysis to determine optimal docking locations, then passenger loading and unloading efficiency is improved, but the computational complexity and processing time for route planning increases
Solution Approach 1:
The vehicle transportation network is partitioned into multiple regions based on docking location cluster medians, allowing the system to process and evaluate docking options in smaller, manageable segments rather than analyzing the entire network at once. This segmentation reduces computational complexity while maintaining the ability to identify optimal docking locations based on pedestrian travel time.
Solution Approach 2:
The system evaluates pedestrian travel time and docking location suitability locally within each partitioned region rather than globally across the entire network. By focusing computational resources on local characteristics of each region, the system efficiently determines optimal docking locations without the prohibitive computational cost of global analysis.
2Measurement precision
If the system analyzes multiple docking locations and performs network partitioning to determine optimal passenger docking locations, then the accuracy of destination association is improved, but the time required for route determination increases
Solution Approach 1:
The system pre-partitions the vehicle transportation network into regions and pre-identifies docking location clusters before actual route planning is needed. This preliminary organization of spatial data allows the system to quickly query and evaluate specific docking locations within pre-defined regions, reducing the time required for real-time route determination while maintaining high accuracy in destination association.
3Productivity
If the autonomous vehicle system implements detailed vehicle transportation network partitioning based on docking location clusters, then the operational efficiency of autonomous vehicle routing is enhanced, but the data processing requirements and system resources increase
Solution Approach 1:
The system extracts and stores only the essential characteristics of each partitioned region, such as docking location cluster medians and key spatial relationships, rather than processing and storing complete detailed maps of the entire transportation network. This extraction of critical data elements reduces memory requirements and data processing needs while maintaining the ability to efficiently determine optimal routing decisions.
Data Source
AI summary
A method and apparatus for associating passenger docking locations with destinations using vehicle transportation network partitioning are disclosed. Associating passenger docking locations with destinations using vehicle transportation network partitioning may include an autonomous vehicle identifying transportation network information representing a vehicle transportation network, the vehicle transportation network including a primary destination, wherein identifying the transportation network information includes identifying the transportation network information such that it includes docking location information representing a plurality of docking locations, wherein each docking location corresponds with a respective location in the vehicle transportation network, such that at least one docking location is associated with the primary destination based on pedestrian travel time, determining a target docking location, identifying a route from an origin to the target docking location in the vehicle transportation network using the transportation network information, and traveling from the origin to the target docking location using the route.