Autonomous Vehicle Dispatch for Fleet Rebalancing and Maintenance
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Solution Overview
Problem
Conventional transportation management systems that utilize autonomous vehicles are limited in their ability to manage fleet-level objectives such as maintenance, vehicle distribution, and service optimization, leading to suboptimal vehicle distribution and increased downtime, which affects the quality of service and ridership experience.
Innovation Solution
A transportation management system that integrates fleet-level management architecture to optimize autonomous vehicle distribution, maintenance, and service by instructing vehicles to drive to service centers or relocate to balance demand, while continuing to fulfill ride requests to minimize downtime and maintain productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles are dispatched to fulfill transportation requests, then service coverage and rider satisfaction are improved, but fleet-level objectives such as maintenance and vehicle distribution cannot be optimized
Solution Approach 1:
The system enables autonomous vehicles to self-manage fleet-level objectives by automatically dispatching themselves to service centers for maintenance when needed, and self-relocating to balance geographic distribution, thereby optimizing fleet productivity without human intervention while continuing to fulfill transportation requests
Solution Approach 2:
The system performs preliminary assessment of fleet status and proactively dispatches autonomous vehicles to service centers or relocation destinations before critical issues arise, ensuring maintenance is performed in advance and vehicle distribution is optimized before demand peaks, thus preventing service disruptions
2Reliability
If autonomous vehicles are taken offline for maintenance and service, then vehicle reliability is improved, but service availability and rider experience deteriorate
Solution Approach 1:
The system monitors vehicle status in real-time and dispatches autonomous vehicles to service centers before failures occur, performing maintenance proactively when vehicles are naturally idle between requests, thus maintaining reliability without significant service availability loss
Solution Approach 2:
The system dynamically adjusts maintenance scheduling based on real-time demand patterns, vehicle usage intensity, and fleet distribution needs, allowing flexible optimization of reliability while minimizing impact on service availability through adaptive decision-making
3Ease of operation
If autonomous vehicles are concentrated in specific geographic regions, then local service quality is improved, but overall fleet efficiency and response to regional demand variations worsen
Solution Approach 1:
The system implements dynamic vehicle redistribution by continuously monitoring demand patterns across different geographic regions and automatically dispatching autonomous vehicles from high-demand to low-demand areas, ensuring optimal local service quality while maintaining overall fleet efficiency through adaptive reallocation
Solution Approach 2:
The system uses real-time feedback from ride requests, vehicle locations, and demand predictions to continuously adjust vehicle distribution, allowing the fleet to respond proactively to regional demand variations while maintaining balanced geographic coverage and high service quality across all areas
Data Source
AI summary
In one embodiment, a method includes determining a fleet-level objective for a vehicle associated with instructing the vehicle to travel a route according to route criteria based on the fleet-level objective. The method includes receiving a ride request from a ride requestor associated with ride criteria including a pick-up location and a drop-off location. The method includes determining that the ride requestor is a passenger for the vehicle to satisfy the fleet-level objective contingent on modifications to the ride criteria. The method includes providing incentives to the ride requestor contingent on acceptance of the modifications to the ride criteria. The method includes, after receiving the acceptance of the modifications to the ride criteria, modifying the ride criteria in accordance with the route criteria. The method includes instructing the vehicle to transport the ride requestor based on the modified ride criteria so as to fulfill the fleet-level objective.