Hybrid Vehicle Scheduling Using Area-Based Dispatch Logic
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Solution Overview
Problem
There is no effective solution in existing technologies for efficiently scheduling autonomous vehicles and conventional manned vehicles to improve user travel efficiency when a travel request is made.
Innovation Solution
A method and apparatus that determine a first area for manned vehicles and a second area for autonomous vehicles based on the user's starting position, using a mesh unit division and order-taking probability evaluation to find available vehicles, and schedule the most suitable vehicle for the user, considering factors like driver availability and travel time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only manned vehicles are used for scheduling, then driver availability and flexibility are maintained, but travel time increases and efficiency decreases
Solution Approach 1:
The scheduling system is segmented into two independent channels: manned vehicle scheduling and autonomous vehicle scheduling. This allows the system to leverage the flexibility of manned vehicles while simultaneously utilizing the time efficiency of autonomous vehicles, thereby resolving the contradiction between travel efficiency and travel time.
Solution Approach 2:
The scheduling system is designed to handle both manned and autonomous vehicles through a unified interface and common evaluation model. This multi-functionality enables the system to adapt to different vehicle types and optimize scheduling based on real-time conditions, improving overall productivity while minimizing travel time loss.
2Productivity
If autonomous vehicles are introduced to improve travel efficiency, then system complexity increases due to hybrid scheduling requirements
Solution Approach 1:
The scheduling system is divided into separate modules for manned vehicles and autonomous vehicles, each with dedicated evaluation and selection logic. This segmentation reduces complexity by avoiding the need for a single complex system to handle all vehicle types simultaneously, while still achieving hybrid scheduling capabilities.
Solution Approach 2:
An order-taking probability evaluation model serves as an intermediary mechanism that standardizes the decision-making process for both manned and autonomous vehicles. This mediator simplifies the scheduling logic by providing a unified criterion for vehicle selection, thereby reducing overall system complexity.
3Reliability
If the search area for available vehicles is expanded to increase vehicle availability, then the area coverage increases but the time to find suitable vehicles increases
Solution Approach 1:
The system performs a partial search within a defined mesh unit area rather than exhaustively searching the entire available area. By limiting the initial search to a reasonable scope and using probability-based evaluation, the system achieves sufficient vehicle availability without incurring excessive search time costs.
Solution Approach 2:
The traditional mechanical approach of expanding search area to find vehicles is replaced with a probability-based evaluation model. This model calculates order-taking probability based on vehicle characteristics and historical data, enabling the system to identify suitable vehicles quickly without physically expanding the search area, thus reducing search time while maintaining availability.
Data Source
AI summary
The present disclosure provides a vehicle scheduling method and apparatus, a device and a storage medium, wherein the method comprises: obtaining a travel request sent by a user; determining a first to-be-selected area according to a starting position carried in the travel request; looking up to find manned vehicles which are located in the first to-be-selected area and in a free state, and broadcasting an order to the found manned vehicles; if no drivers take the order, determining a second to-be-selected area according to the starting position; looking up to find autonomous vehicles which are located in a second to-be-selected area and in a free state; selecting one from the found autonomous vehicles and scheduling said one autonomous vehicle to the user. The solution of the present disclosure can be applied to improve the user's travel efficiency.