Autonomous Vehicle Experience Personalization via Network Slicing

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Solution Overview

Problem

Conventional autonomous vehicle technologies fail to effectively tailor the passenger experience according to individual preferences, lacking customization options for interior environments, route conditions, and service classes.

Innovation Solution

A dynamic recommendation engine configures the autonomous vehicle's interior environment through a network slice with virtual network functions, using subscriber profiles and route information to generate configuration data for temperature, seating, multimedia, and navigation settings, enabling personalized experiences and detours based on traffic and other route conditions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional autonomous vehicle technologies are used, then the vehicle can transport passengers without direct control, but the passenger experience cannot be tailored according to individual preferences

Engineering Contradiction:

Improvecustomization of passenger experienceVSAvoidsystem configuration complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by obtaining subscriber profile information and route information before configuring the vehicle. The dynamic recommendation engine pre-processes data from multiple sources including subscriber preferences, route conditions, traffic data, and weather information to generate optimized configuration recommendations in advance of the journey

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements dynamics by continuously adapting vehicle configurations based on real-time conditions. The dynamic recommendation engine adjusts interior environment settings, route recommendations, and service parameters dynamically according to changing route conditions, traffic patterns, and subscriber preferences, transforming static vehicle configurations into adaptive, living systems

Inventive Principle:

Principle #15Dynamics

2Ease of operation

If the vehicle interior environment is customized according to passenger preferences, then passenger comfort is improved, but system complexity increases

Engineering Contradiction:

Improvepassenger comfortVSAvoidconfiguration system complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system implements self-service by automatically configuring vehicle parameters based on subscriber profiles and route information without requiring manual passenger input. The dynamic recommendation engine autonomously processes subscriber preferences, analyzes route conditions, and generates configuration recommendations for interior environment settings, eliminating the need for complex manual configuration interfaces while maintaining high passenger comfort

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system employs feedback mechanisms by continuously monitoring subscriber profiles, route conditions, and vehicle performance data. The dynamic recommendation engine uses this feedback to iteratively optimize configuration recommendations, adjusting interior environment settings based on real-time conditions and historical passenger preferences, thereby improving comfort while managing system complexity through data-driven automation

Inventive Principle:

Principle #23Feedback

3Productivity

If route conditions are considered for configuration, then transportation efficiency is improved, but data processing requirements increase

Engineering Contradiction:

Improvetransportation efficiencyVSAvoiddata processing load

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system applies segmentation by dividing the data processing task into distinct modules within the dynamic recommendation engine. Each module processes specific types of data independently: subscriber profile analysis, route condition evaluation, traffic pattern recognition, and configuration generation. This modular approach enables efficient processing of multiple data sources while maintaining manageable computational loads and improving transportation efficiency through specialized processing pipelines

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11371855B2Dynamic customization of an autonomous vehicle experience

Publication Date: 2022.06.28 AT&T INTELLECTUAL PROPERTY I L P

AI summary

Dynamic customization of an autonomous vehicle experience is presented herein. A dynamic recommendation engine can comprise a subscriber interface component, a data component, and a configuration component. The subscriber interface component can receive, from a subscriber of an autonomous vehicle service, a request specifying a route of transport by an autonomous vehicle; and based on the request, the data component can obtain, via a network slice comprising a virtual network function of the autonomous vehicle service, profile information for the subscriber and route information for the route. Further, the configuration component can determine, via the network slice based on the profile information and the route information, configuration data for the autonomous vehicle, and send, via the network slice, the configuration data directed to the autonomous vehicle to facilitate the transport by the autonomous vehicle.