Autonomous Vehicle Ride Personalization via Network-Slice Configuration
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Solution Overview
Problem
Conventional autonomous vehicle technologies fail to effectively tailor the driving experience to passenger preferences, lacking customization based on individual profiles and route-specific conditions.
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, allowing for personalized and optimized travel experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional autonomous vehicle technologies are used, then the vehicle can transport passengers without direct control, but the driving experience cannot be tailored to individual passenger preferences
Solution Approach 1:
The system performs preliminary actions by obtaining subscriber profile information and route information before configuring the autonomous vehicle. The dynamic recommendation engine pre-processes passenger preferences, historical data, and route conditions to generate optimized configuration recommendations in advance, enabling personalized experience without adding operational complexity during the actual ride.
Solution Approach 2:
The patent introduces a dynamic recommendation engine as an intermediary component between the passenger and the autonomous vehicle system. This mediator processes passenger profiles, analyzes route conditions, and generates configuration recommendations that bridge the gap between basic transportation functionality and personalized experience customization.
2Ease of operation
If the system configures the vehicle based on multiple factors (profile, route, service class), then the passenger experience is enhanced, but the processing complexity increases
Solution Approach 1:
The configuration process is segmented into distinct components: obtaining subscriber profile information, obtaining route information, determining configuration data based on multiple factors, and sending recommendations. This segmentation allows the system to handle complex multi-factor analysis through modular processing steps, making the overall system more manageable while delivering enhanced passenger experience.
3Productivity
If real-time configuration data is generated and sent to the vehicle, then the journey is optimized for comfort and efficiency, but the network and processing load increases
Solution Approach 1:
The system enables self-service by allowing the autonomous vehicle to automatically receive and implement configuration recommendations without requiring manual intervention from passengers or operators. The vehicle's systems (climate control, seating, entertainment, navigation) self-adjust based on the generated configuration data, optimizing journey comfort and efficiency while minimizing the energy required for manual configuration processes.
Data Source
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.