Autonomous Vehicle Docking Location Selection via Pedestrian Travel Time
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Solution Overview
Problem
Autonomous vehicles lack efficient methods for selecting optimal passenger docking locations, which affects navigation efficiency and passenger experience due to the absence of integrated pedestrian travel time considerations in existing routing systems.
Innovation Solution
An autonomous vehicle system that identifies and selects a target docking location based on transportation network information, incorporating pedestrian travel time, using a processor to determine the most suitable location and generate routes, and a trajectory controller to navigate to the selected docking location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicle routing systems use traditional navigation methods, then the vehicle can reach the destination, but the system lacks optimization for passenger docking location selection and does not consider pedestrian travel time
Solution Approach 1:
The routing system is segmented into multiple functional modules: a transportation network information identification module that processes vehicle routing data, a pedestrian travel time calculation module that computes walking times from multiple docking locations to the destination, and a target docking location selection module that integrates both data streams. This segmentation allows the system to handle complex multi-criteria optimization without overwhelming computational burden.
Solution Approach 2:
The system performs preliminary calculations of pedestrian travel times from each candidate docking location to the destination before finalizing the routing decision. By pre-computing these pedestrian accessibility metrics and storing them alongside vehicle routing information, the system prepares optimized docking location selections in advance, improving real-time navigation efficiency.
2Ease of operation
If the system selects docking locations without considering pedestrian travel time, then the vehicle routing is simplified, but the passenger experience deteriorates due to potentially long walking distances
Solution Approach 1:
The system transitions from traditional single-dimension vehicle-centric routing to a multi-dimensional approach that simultaneously considers vehicle travel time and pedestrian walking time. By adding the pedestrian travel time dimension to the routing optimization, the system identifies docking locations that minimize total passenger transit time, thereby improving ease of operation without sacrificing time efficiency.
Solution Approach 2:
The system changes the optimization parameters from solely vehicle-based metrics to a combined vehicle-pedestrian metric system. By incorporating pedestrian travel time as a key parameter alongside vehicle routing distance and time, the system dynamically selects docking locations that optimize the complete passenger journey, enhancing both passenger experience and time efficiency.
3Measurement precision
If the system evaluates multiple candidate docking locations with pedestrian travel time, then docking location optimization improves, but the computational complexity increases
Solution Approach 1:
The system introduces an intermediary computational layer that pre-processes and stores pedestrian travel time data for multiple candidate docking locations. This intermediary data structure acts as a bridge between the transportation network information and the final selection algorithm, enabling precise multi-criteria comparison without requiring complex real-time calculations during route planning.
Solution Approach 2:
The system performs preliminary evaluation and ranking of multiple candidate docking locations based on integrated vehicle and pedestrian travel time metrics before final selection. By pre-computing and storing these optimized rankings, the system achieves high measurement precision in docking location selection while reducing the computational complexity of real-time decision-making.
Data Source
AI summary
A method and apparatus for passenger docking location selection are disclosed. Passenger docking location selection 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, and such that at least one docking location is associated with the primary destination, determining a target docking location for the primary destination based on the transportation network information and pedestrian travel time, identifying a first 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 first route.