MaaS Vehicle Dispatch Planning for Predicted ODD Violations
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Solution Overview
Problem
Autonomous vehicles in Mobility-as-a-Service (MaaS) operations face challenges when their operational design domain (ODD) is not satisfied, leading to frequent and costly recoveries, as existing techniques require pre-determined secure areas and are not adaptable to dynamic factors.
Innovation Solution
A system that generates and modifies dispatch plans for MaaS vehicles by predicting ODD satisfaction and using pilot vehicles or vehicles with drivers to navigate autonomous vehicles out of unsatisfactory conditions, reducing recovery frequency and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicles operate without pre-determined secure areas, then adaptability to dynamic factors improves, but recovery frequency increases
Solution Approach 1:
The system performs preliminary identification of inoperable vehicles by predicting future ODD satisfaction status before actual operational failures occur. This allows proactive dispatch plan modification to prevent recovery situations, rather than reacting after failures happen.
Solution Approach 2:
The system dynamically modifies dispatch plans based on real-time and predicted ODD satisfaction status. Unlike static pre-determined secure areas, the system adapts vehicle assignments and routes dynamically according to changing environmental conditions and vehicle operational status.
2Loss of time
If pilot vehicles are kept in second geographical area, then recovery time reduces, but operation cost increases
Solution Approach 1:
The system identifies and designates pilot vehicles in advance based on predicted ODD violations before they occur. This preliminary designation allows rapid response when violations happen, reducing recovery time without requiring continuous presence of pilot vehicles in potential problem areas.
Solution Approach 2:
The system enables autonomous vehicles to self-identify when they will violate ODD conditions through prediction, and self-coordinate with designated pilot vehicles. This reduces the need for continuous human monitoring and manual intervention, lowering operational costs while maintaining quick response capability.
3Adaptability or versatility
If autonomous vehicles with wide ODD are deployed, then service coverage improves, but vehicle cost increases
Solution Approach 1:
The system makes standard autonomous vehicles multi-functional by enabling them to operate in both ODD-satisfied and ODD-violating conditions through dynamic dispatch plan modification. Vehicles don't need specialized wide-ODD configurations because the system adapts their operational parameters dynamically.
Solution Approach 2:
The system changes operational parameters dynamically by modifying dispatch plans when ODD violations are predicted. This allows standard vehicles to effectively adapt their operating conditions without requiring expensive hardware modifications for expanded ODD coverage.
Data Source
AI summary
A system comprises a plurality of MaaS vehicles, one or more processors configured to execute generating a dispatch plan for the plurality of MaaS vehicles. The plurality of MaaS vehicles includes one or more autonomous vehicles performing autonomous driving in accordance with the dispatch plan. The generating the dispatch plan includes acquiring an operational design domain of the one or more autonomous vehicles, acquiring a prediction of dispatch service environment for a predetermined period of time in the future regarding a dispatch service area of the one or more autonomous vehicles, specifying an inoperable vehicle among the one or more autonomous vehicles based on the operational design domain and the prediction of dispatch service environment, and modifying the dispatch plan depending on vehicle information or service information of the inoperable vehicle.