Mixed Fleet Service Assignment for AV-Suitable Ride Requests
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Solution Overview
Problem
Managing a mixed fleet of vehicles that includes autonomous and human-operated vehicles presents challenges in assigning transportation services effectively, as user preferences and request properties may make certain services unsuitable for autonomous vehicle execution, leading to inefficiencies and user dissatisfaction.
Innovation Solution
A service assignment system that prompts users to modify their preferences or request properties to make services suitable for autonomous vehicle execution, thereby increasing the pool of candidate vehicles and improving service utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the service assignment system strictly follows user preferences and request properties, then user satisfaction is improved, but autonomous vehicle utilization decreases
Solution Approach 1:
The system performs preliminary analysis of user preferences and request properties before service assignment, identifying opportunities to modify preferences that would enable AV execution. By proactively suggesting modifications to pickup/dropoff locations or times, the system prepares the ground for AV assignment while maintaining user satisfaction.
Solution Approach 2:
The system dynamically adjusts service parameters such as pickup location, dropoff location, and service time based on AV capabilities and availability. By modifying these parameters within acceptable ranges, the system makes services compatible with AV execution without significantly impacting user experience.
2Productivity
If the service assignment system expands the pool of candidate vehicles to include more autonomous vehicles, then autonomous vehicle utilization increases, but service suitability decreases
Solution Approach 1:
The system implements a feedback loop where user preferences and AV capabilities are continuously analyzed. The system provides feedback to users about how preference modifications would enable AV execution, and adjusts assignments based on the outcomes, progressively improving service suitability while maintaining high AV utilization.
Solution Approach 2:
The system dynamically evaluates service suitability based on real-time AV availability, capabilities, and user preferences. Rather than using static criteria, the system adapts its assessment of service suitability to maximize AV utilization while maintaining acceptable service quality through flexible parameter adjustment.
3Productivity
If the service assignment system modifies user preferences to enable autonomous vehicle execution, then autonomous vehicle utilization increases, but system complexity increases
Solution Approach 1:
The system segments the preference modification process into distinct, manageable components: analyzing user preferences, identifying modification opportunities, suggesting specific changes, and evaluating outcomes. This modular approach reduces system complexity by breaking down the complex decision-making process into discrete, independently manageable steps.
Data Source
AI summary
Various examples are directed to systems and methods for managing a mixed fleet of vehicles to execute transportation services. A system may access transportation service request data describing a transportation service requested by a user via a user computing device. The system may determine that the first transportation service is not suitable for execution by at least one of a plurality of autonomous vehicles (AV). The system may prompt the user via the user computing device to make a modification to the transportation service to make it suitable for execution by the at least one of the plurality of AVs.