Autonomous Fleet Compatibility Screening for Reliable Ride Assignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing autonomous vehicle systems face inefficiencies in matching users with compatible vehicle fleets, leading to potential service cancellations, unnecessary resource allocation, and suboptimal route planning due to limitations in autonomy capabilities and user preferences.
Innovation Solution
A computer-implemented method and system that determine compatibility between users and autonomous vehicle fleets by analyzing user and vehicle fleet features, including autonomy capabilities and user preferences, to select the most suitable fleet for vehicle services, thereby optimizing resource usage and ensuring route adherence within vehicle capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If autonomous vehicle systems assign vehicles to users without compatibility analysis, then service speed is improved, but service reliability deteriorates due to potential cancellations and route failures
Solution Approach 1:
The system performs compatibility analysis between user profiles and vehicle fleet features before assigning vehicles. This preliminary action includes comparing user preferences, autonomy capability requirements, and vehicle capabilities to predict service success and prevent cancellations, thereby improving reliability without significantly impacting service speed
Solution Approach 2:
The patent introduces a compatibility prediction mechanism as an intermediary between user service requests and vehicle assignment. This intermediary analyzes user profiles against vehicle fleet features and autonomously determines compatibility, enabling fast and reliable assignments without requiring manual intervention or post-assignment adjustments
2Reliability
If the system analyzes all user and vehicle features to determine compatibility, then service reliability is improved, but computational resource usage increases
Solution Approach 1:
The system extracts and analyzes only the critical compatibility features from user profiles and vehicle fleet data, rather than processing all available information. By identifying and focusing on key parameters that determine compatibility, the system achieves reliable vehicle-user matching with reduced computational overhead
Solution Approach 2:
The patent transforms the compatibility determination problem into a parameter-based prediction model. By changing the approach from comprehensive analysis to parameter-specific evaluation, the system efficiently assesses compatibility using selected key parameters, thereby reducing computational resource consumption while maintaining service reliability
3Manufacturing precision
If the system predicts vehicle performance based on autonomy capabilities, then service quality is improved, but system complexity increases
Solution Approach 1:
The system creates simplified predictive models that replicate the relationship between autonomy capabilities and vehicle performance. Instead of implementing complex real-time simulation, the patent uses pre-established prediction models that copy the essential performance characteristics, enabling high service quality determination with reduced system complexity
Solution Approach 2:
The patent simplifies the performance prediction by focusing on key autonomy capability parameters rather than analyzing all vehicle systems in detail. By changing the approach to evaluate only the most critical autonomy parameters relevant to service quality, the system achieves accurate predictions without requiring overly complex analysis mechanisms
Data Source
AI summary
Systems and methods for autonomous vehicle operations are provided. An example computer-implemented method includes obtaining data indicative of vehicle fleet feature(s) associated with an autonomous vehicle fleet. The method includes obtaining data indicative of a vehicle service request associated with a user, the vehicle service request indicating a request for a vehicle service. The method includes determining user feature(s) associated with the user. The method includes determining a compatibility of the user and the autonomous vehicle fleet for the vehicle service based at least in part on the fleet feature(s) and the user feature(s). Determining the compatibility can include predicting how the autonomous vehicle fleet will perform the vehicle service associated with the vehicle service request based at least in part on the fleet's autonomy capabilities. The method includes communicating data associated with the vehicle service request to a computing system associated with the autonomous vehicle fleet.