Autonomous Vehicle Joint Routing for Capability-Aware Service Assignment
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Solution Overview
Problem
Existing service assignment systems for autonomous vehicles face challenges in accurately routing transportation services due to variations in vehicle capabilities and policies, leading to sub-optimal routes and deviations from expected execution times.
Innovation Solution
Implementing a joint routing system where the service assignment system generates proposed routes for multiple autonomous vehicles, allowing them to indicate their intended routes, and selecting the most suitable vehicle based on factors like time, risk, and user preferences, while considering vehicle capabilities and policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the service assignment system assumes a standard route for autonomous vehicles, then the routing process is simple and fast, but the actual route execution deviates due to vehicle-specific capabilities and policies
Solution Approach 1:
The routing process is segmented into two independent phases: (1) service assignment system generates proposed routes without vehicle-specific constraints, and (2) autonomous vehicle determines final intended routes based on its capabilities and policies. This segmentation allows each system to operate optimally without compromising the other.
Solution Approach 2:
The service assignment system performs preliminary route generation before vehicle selection, creating a set of proposed routes that serve as initial options. This preliminary action enables efficient service assignment while allowing subsequent route adjustment by the vehicle.
2Productivity
If the service assignment system dictates the exact route for autonomous vehicles, then route optimization is maximized, but the vehicle operator loses flexibility to adjust based on vehicle capabilities and policies
Solution Approach 1:
The routing system transitions from a static, centralized route assignment to a dynamic, two-stage process where the route evolves from proposed to intended based on vehicle-specific factors. This dynamic approach balances optimization with operational flexibility.
Solution Approach 2:
The system merges the service assignment system's route optimization capabilities with the autonomous vehicle's capability and policy considerations. Both systems contribute to the final intended route, combining centralized efficiency with decentralized adaptability.
3Loss of time
If the service assignment system selects vehicles based on expected route execution time, then service assignment efficiency is improved, but actual execution time varies due to route deviations
Solution Approach 1:
The system incorporates feedback from the autonomous vehicle's capability and policy assessment into the route determination process. This feedback loop allows the vehicle to communicate its actual route preferences, improving the accuracy of execution time estimates while maintaining efficient service assignment.
Data Source
AI summary
Various examples are directed to a service assignment system for providing transportation services. The service assignment system may receive a transportation service request from a user. The transportation service request may describe a transportation service having a service start location and a service end location. The service assignment system may generate a plurality of routes for executing the transportation service. The service assignment system may send proposed route data describing at least a portion of the plurality of routes to a first autonomous vehicle (AV). The service assignment system may receive, from the first AV, route data describing a first route to execute the transportation service and send, to the first AV, instruction data instructing the first AV to begin executing the transportation service.