Vehicle Configuration via Sensor-Based Attribute Recognition
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Solution Overview
Problem
Vehicle occupants often waste resources and experience inefficiency when manually adjusting vehicle settings such as seat position, temperature, and radio stations, leading to unnecessary wear and tear on vehicle components and a suboptimal driving experience.
Innovation Solution
A vehicle system utilizing on-board processors to analyze image and sensor data using machine learning to identify occupants and determine preferred vehicle configurations, automatically adjusting settings based on their attributes and location relative to the vehicle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If vehicle settings are manually adjusted by occupants, then the vehicle can be configured to individual preferences, but resources are wasted and unnecessary wear and tear occurs on vehicle components
Solution Approach 1:
The system performs preliminary action by automatically configuring vehicle settings before the occupant needs them. Sensors detect the occupant's approach and attributes, and the system pre-adjusts seats, temperature, and other settings based on predicted preferences, eliminating the need for manual trial-and-error adjustments and reducing energy waste.
Solution Approach 2:
The vehicle system provides self-service by autonomously configuring its own settings without human intervention. The processor analyzes sensor data including image data and attributes of the occupant, determines preferred configurations, and automatically adjusts vehicle components, allowing the vehicle to serve itself rather than relying on manual occupant adjustment.
2Ease of operation
If manual adjustment of vehicle settings is performed, then individual preferences can be achieved, but time is lost through trial-and-error adjustments
Solution Approach 1:
The system performs preliminary action by automatically configuring vehicle settings before the occupant needs them. Sensors detect the occupant's approach and attributes, and the system pre-adjusts seats, temperature, and other settings based on predicted preferences, eliminating the need for manual trial-and-error adjustments and reducing energy waste.
Solution Approach 2:
The system uses feedback from sensor data including image data, attribute recognition, and machine learning analysis to continuously improve configuration accuracy. The processor analyzes occupant attributes and location, determines preferred settings, and adjusts vehicle components accordingly, creating a feedback loop that reduces adjustment time with each use.
3Loss of energy
If automatic vehicle configuration is implemented using sensor data and machine learning, then resource efficiency improves and wear on components is reduced, but device complexity increases
Solution Approach 1:
The system applies universality by using a single integrated processor that performs multiple functions: analyzing image data, recognizing attributes, determining preferences through machine learning, and controlling various vehicle components. This multi-functional approach reduces the need for separate dedicated systems for each function, managing complexity while achieving resource efficiency.
Solution Approach 2:
The system replaces manual mechanical adjustment with automated electronic control based on sensor data and machine learning. The processor electronically configures vehicle settings rather than requiring physical manual adjustment, reducing wear on mechanical components while managing system complexity through intelligent automation.
4Ease of operation
If trial-and-error manual adjustments are made to configure vehicle settings, then occupants can find preferred configurations, but productivity and speed of vehicle preparation are reduced
Solution Approach 1:
The system performs preliminary action by automatically configuring vehicle settings before the occupant needs them. Sensors detect the occupant's approach and attributes, and the system pre-adjusts seats, temperature, and other settings based on predicted preferences, eliminating the need for manual trial-and-error adjustments and reducing energy waste.
Solution Approach 2:
The vehicle system provides self-service by autonomously configuring its own settings without human intervention. The processor analyzes sensor data including image data and attributes of the occupant, determines preferred configurations, and automatically adjusts vehicle components, allowing the vehicle to serve itself rather than relying on manual occupant adjustment.
Data Source
AI summary
A vehicle receives sensor data that includes image data of frames that depict one or more individuals outside of the vehicle, and identifies, by analyzing the sensor data using one or more attribute recognition techniques, a set of attributes of an individual of the one or more individuals. The vehicle determines a set of scores indicating a set of likelihoods of a set of vehicle configurations being a preferred vehicle configuration for the individual, based on a data model performing a machine-learning-driven analysis of attribute data identifying the set of attributes, and/or location data identifying a location of the individual relative to the vehicle. The vehicle selects a particular vehicle configuration based on a score that indicates a likelihood of the particular vehicle configuration being the preferred vehicle configuration and provides an instruction to cause a vehicle component to implement the particular vehicle configuration by updating a configurable value.