Autonomous Vehicle Interior Configuration via Preliminary Action
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Solution Overview
Problem
Autonomous vehicles (AVs) lack efficient systems for pre-configuring interior settings based on user preferences, leading to suboptimal comfort and increased energy consumption due to manual adjustments by drivers and passengers in each use.
Innovation Solution
A transport facilitation system that uses user input, accelerometer data, and machine learning to preemptively configure AV settings, including seat adjustments, temperature, and climate control, optimizing timing for configuration based on user preferences and real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the AV configures interior settings manually for each use, then the system complexity remains low, but user comfort deteriorates due to lack of personalization
Solution Approach 1:
The system performs preliminary configuration of interior settings by determining user preferences in advance and transmitting configuration commands to the AV before the user arrives. This preliminary action ensures personalized comfort settings are ready when the user enters the vehicle, resolving the contradiction by providing customization without requiring complex real-time adjustments.
Solution Approach 2:
The system enables self-service configuration by automatically determining user preferences through accelerometer data analysis and machine learning, then autonomously transmitting configuration commands to the AV. This self-service approach eliminates manual adjustment complexity while delivering personalized comfort, as the system serves itself by learning and applying user preferences automatically.
2Ease of operation
If the AV configures settings for each use manually, then energy consumption is reduced, but user comfort deteriorates due to delayed personalization
Solution Approach 1:
The system performs preliminary configuration of interior settings before the user arrives at the AV. By determining user preferences in advance and transmitting configuration commands proactively, the system ensures personalized comfort is ready without requiring energy-intensive real-time adjustments once the user is in the vehicle, thus improving comfort while managing energy consumption efficiently.
3Use of energy by moving object
If the AV optimizes timing for configuration, then energy consumption is reduced, but configuration accuracy may deteriorate if timing is not precise
Solution Approach 1:
The system uses periodic action by transmitting configuration commands at optimally timed intervals before the user arrives at the AV. This periodic timing ensures configuration is completed at the right moment - early enough to save energy by avoiding last-minute adjustments, but late enough to ensure accuracy. The system periodically checks and transmits commands based on predicted arrival times, balancing energy efficiency with configuration precision.
4Ease of operation
If the system uses machine learning to learn user preferences, then user comfort improves over time, but device complexity increases due to data processing requirements
Solution Approach 1:
The system implements self-service by automatically determining user preferences through accelerometer data analysis using machine learning algorithms. The system serves itself by learning from user behavior patterns and autonomously generating configuration commands without requiring manual input or complex real-time processing. This self-learning approach improves user comfort over time while managing data processing complexity through automated preference determination.
Data Source
AI summary
An autonomous vehicle (AV) can receive a pick-up location to rendezvous with a rider and a set of configuration instructions to configure one or more adjustable components of the configurable interior system for the rider. The AV can analyze sensor data to autonomously control acceleration, steering, and braking systems along a route to the pick-up location. Prior to arriving at the pick-up location, the AV can execute the set of configuration instructions to configure the one or more adjustable components of the configurable interior system for the rider.