Autonomous Vehicle Trip Pre-Assignment for Shorter Pickup Times
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Solution Overview
Problem
Current autonomous vehicle dispatching systems lack efficient trip planning capabilities, leading to increased 'empty miles,' longer pickup times, and reduced demand for trips, as they rely on user-initiated requests and do not effectively utilize historical user data to optimize vehicle positioning and scheduling.
Innovation Solution
A method for advanced trip planning that uses server computing devices to determine potential pickup and destination locations, assign vehicles, and provide trip information to users, including estimated times of arrival and costs, while utilizing historical user scores to rank and pre-assign vehicles, thereby reducing 'empty miles' and optimizing vehicle positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user-initiated requests are used for trip planning, then system simplicity is maintained, but empty miles increase and pickup times lengthen
Solution Approach 1:
The system performs preliminary trip planning by proactively suggesting trips to users before they initiate requests. The server determines potential pickup locations, destination locations, and assigns vehicles in advance, preparing trip information and presenting it to users for confirmation. This preliminary action reduces pickup times and optimizes vehicle positioning before actual trips occur.
2Productivity
If historical user data is not utilized, then data processing complexity is reduced, but vehicle positioning optimization is impaired
Solution Approach 1:
The system incorporates feedback loops that utilize historical user data to continuously improve trip planning. The server accesses historical information about user trip patterns, preferences, and behaviors to inform proactive trip suggestions and vehicle assignments. This feedback mechanism enables the system to learn and adapt, optimizing vehicle positioning and trip planning over time while managing data processing through efficient algorithms.
3Productivity
If proactive trip suggestion is implemented, then ridership increases, but system complexity increases
Solution Approach 1:
The system segments the trip planning process into distinct functional modules: a determination module that identifies potential trips based on historical data and user profiles, an assignment module that allocates vehicles to suggested trips, and a presentation module that displays trip information to users. This segmentation allows the complex proactive suggestion system to be managed through specialized, interoperable components, reducing overall system complexity while maintaining high ridership capabilities.
Data Source
AI summary
Aspects of the disclosure provide for advanced trip planning for an autonomous vehicle service. For instance, an example method may include determining a potential pickup location for a user, determining a set of potential destination locations for a user, and determining a set of potential trips. For each potential trip a vehicle of a fleet of autonomous vehicles of the service may be assigned and trip information, including an estimated time of arrival for the assigned vehicle of the potential trip to reach the destination location of the potential trip, may be determined. The trip information for each potential trip may be provided for display to the user. Thereafter, confirmation information identifying one of the set of potential trips may be received, and the assigned vehicle for one first of the set of potential trips may be dispatched to pick up the user.