Self-Driving Car Scheduling Using Real-Time Road Status
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Solution Overview
Problem
Current self-driving car scheduling methods are inefficient due to reliance on limited location-based references, leading to low scheduling precision, as they do not account for real-time traffic conditions or road status, which can affect the timely arrival of cars at ride requests.
Innovation Solution
A self-driving car scheduling method that considers ride requests including starting and destination locations, expected arrival times, and current road status information to determine the most suitable car, incorporating real-time traffic data and route planning to ensure precise scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If car scheduling is performed according to only locations of assigned cars and ride request, then the scheduling process is simple, but scheduling precision is low
Solution Approach 1:
The patent expands the scheduling parameters from simple location-based matching to include real-time traffic conditions, road status, car status, and multiple route options. This parameter enrichment enables more precise scheduling decisions while maintaining computational feasibility through structured data organization and prioritization mechanisms.
2Measurement precision
If real-time traffic conditions and road status are considered in scheduling, then scheduling precision is improved, but the complexity of data processing increases
Solution Approach 1:
The system performs preliminary data collection and organization of traffic conditions, road status, and car information before the actual scheduling decision. By pre-processing and structuring this data, the system reduces the computational burden during real-time scheduling while maintaining high precision in matching cars to ride requests.
Solution Approach 2:
The patent introduces an intermediary scheduling system that acts as a mediator between raw traffic data and final car assignment decisions. This intermediary layer processes and synthesizes multiple data sources (traffic conditions, road status, car status) into actionable scheduling parameters, simplifying the overall system architecture while improving decision accuracy.
3Measurement precision
If multiple reference factors are used for car scheduling, then scheduling precision is enhanced, but the computational time increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on the most relevant factors for each specific scheduling scenario. Rather than uniformly processing all possible parameters for every ride request, the system identifies and prioritizes the most critical local factors (such as current traffic conditions near the pickup location or car availability in the vicinity), thereby reducing overall computational time while maintaining high scheduling precision.
Data Source
AI summary
A self-driving car scheduling method, a car scheduling server, and a self-driving car includes receiving a ride request; determining, according to the ride request and driving information of self-driving cars within a management range, at least one first candidate car from the multiple self-driving cars; calculating a first time required by each first candidate car to arrive at a ride destination according to current location information, current road status information, and planned route information of each first candidate car; determining a final candidate car from the at least one first candidate car according to the first time corresponding to each first candidate car.