Autonomous Vehicle Fleet Routing for Intermediate Stop Wait Time
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Solution Overview
Problem
Autonomous vehicles face inefficiencies when making multiple destination trips due to prolonged waiting times at intermediate stops, which can waste vehicle time and reduce the number of trips serviced.
Innovation Solution
A fleet management system optimizes vehicle utilization by determining how long a vehicle will wait at intermediate destinations, assigning maintenance or short/long duration trips based on waiting time, and ensuring the vehicle remains proximate to the destination for immediate passenger pickup.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an autonomous vehicle performs multiple errands in a single trip, then productivity and resource utilization are improved, but trip routing complexity and scheduling difficulty increase
Solution Approach 1:
The system segments the complex multi-destination trip planning problem into discrete, manageable components: individual errands with specific parameters (location, time window, duration). Each errand is treated as an independent task that can be individually evaluated and optimized, allowing the routing system to process multiple errands through systematic algorithms rather than facing overwhelming complexity.
Solution Approach 2:
The system performs preliminary actions by pre-processing errand data, pre-calculating optimal routes, and pre-scheduling trips before execution. The server receives errand information in advance, processes routing options, and prepares optimized trip plans, so that when trips are executed, the complexity has already been resolved through prior computational work.
2Device complexity
If traditional routing algorithms are used for multi-destination trips, then implementation simplicity is maintained, but travel time and resource efficiency deteriorate
Solution Approach 1:
The system transitions from static, fixed routing algorithms to dynamic, adaptive routing that responds to real-time conditions. The server continuously receives updates on errand completion, vehicle location, and changing conditions, then dynamically recalculates and adjusts trip routes to optimize travel time and efficiency throughout the journey.
Solution Approach 2:
The system implements feedback mechanisms where the server monitors trip progress, receives status updates from vehicles, and uses this information to continuously optimize routing decisions. The feedback loop allows the system to learn from actual trip outcomes and improve future routing efficiency, converting the complexity of multi-destination trips into measurable performance improvements.
Data Source
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AI summary
Aspects of the disclosure relate to a method of managing a fleet of autonomous vehicles providing trip services. The method includes receiving information identifying an intermediate destination and a final destination for a trip 900. In this example, the intermediate destination 920 is a destination where an autonomous vehicle 100 will drop off and wait for a passenger in order to continue the trip, and the final destination 930 is a destination where the trip ends. The method also includes determining an amount of waiting time the vehicle is likely to be waiting for the passenger at the intermediate destination, determining how a vehicle of the fleet of autonomous vehicles should spend the amount of waiting time, and sending an instruction to the vehicle, based on the determination of how the vehicle should spend the amount of waiting time.