Vehicle Configuration Using User Data Analysis
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Solution Overview
Problem
Current autonomous vehicle systems lack the ability to dynamically configure actions based on user-specific data, such as preferences and mood, leading to suboptimal user experiences and vehicle operations.
Innovation Solution
Implementing an Artificial Neural Network (ANN) model that collects and analyzes user data from various sources, including sensors in the user's environment and mobile devices, to tailor vehicle settings and operations, such as infotainment systems and driving styles, using machine learning and natural language processing to determine user preferences and mood.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous vehicle systems use standard control algorithms without user-specific customization, then system complexity is reduced, but user experience and operational optimization are compromised
Solution Approach 1:
The system segments the autonomous vehicle control into multiple independent modules: data collection module, machine learning model module, and control action module. This allows the system to collect and process user-specific data without redesigning the entire control system, thereby improving adaptability while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary data collection and user profiling before actual vehicle operation. By pre-processing user data and training machine learning models in advance, the system prepares personalized control parameters ahead of time, enabling rapid adaptation during vehicle operation without adding real-time computational complexity.
2Ease of operation
If the vehicle collects and analyzes extensive user data, then personalized configuration and user experience are improved, but data privacy concerns and system complexity increase
Solution Approach 1:
The system applies different processing levels to different types of data: sensitive personal information is processed locally on the vehicle with minimal external transmission, while anonymized operational data can be shared for model improvement. This selective data handling protects user privacy while still enabling personalized vehicle operation through local analysis of necessary information.
3Productivity
If the system continuously monitors and adapts to user behavior, then user experience optimization is improved, but computational resource consumption and system complexity increase
Solution Approach 1:
The system implements periodic rather than continuous data processing: user behavior is monitored continuously but analyzed in periodic batches at appropriate intervals (e.g., after completed trips or at charging stops). This approach maintains up-to-date personalized configurations while significantly reducing computational energy consumption compared to real-time continuous analysis.
Data Source
AI summary
A vehicle is configured to perform at least one action (e.g., control of acceleration or navigation of the vehicle) based on analysis of data that is collected regarding a user of the vehicle. In one embodiment, the data is collected from various sources (e.g., computing devices associated with the user) prior to usage of the vehicle by the user. For example, data may be collected from an intelligent appliance located in a building or other fixed structure in which the user lives, or in which the vehicle is stored or charged. The data collected from the fixed structure may relate to activities performed by the user while inside the fixed structure and/or relate to data associated with electronic communications of the user.