Personalized Driving Unit for Automated Vehicles
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Solution Overview
Problem
Current automated vehicle systems lack personalized driving capabilities, relying on generic user profiles generated from collective driving information, which limits their ability to provide tailored driving experiences.
Innovation Solution
The implementation of a personalized driving unit within the automated vehicle system that tracks user driving habits and preferences, using context extraction and machine learning models to optimize driving actions and routes based on individual behavior and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a generic user profile is used for automated driving, then the system can be implemented with current technology, but the driving experience is not personalized
Solution Approach 1:
The system performs preliminary actions by collecting driving information during manual driving phases and generating personalized driving models before automated driving begins. The personalized driving unit stores user-specific driving habits, preferences, and behavioral patterns in advance, so that when automated driving is activated, the system already has a customized model ready to execute personalized driving actions without requiring complex real-time adaptation mechanisms.
2Loss of information
If driving information from many users is aggregated, then a generic profile can be created, but individual user preferences are lost
Solution Approach 1:
The system segments driving information into two distinct categories: collective driving information used for general safety and basic driving patterns, and individual user-specific driving information used for personalization. The personalized driving unit processes and stores user-specific information separately from aggregate data, ensuring that individual preferences, habits, and behavioral patterns are preserved and not lost in aggregation. This segmentation allows the system to maintain both general knowledge from many users and specific knowledge about each individual user.
3Adaptability or versatility
If the automated vehicle uses collective driving data, then safety can be maintained through proven patterns, but the driving style cannot be customized to individual preferences
Solution Approach 1:
The system implements dynamics by creating a adaptable personalized driving model that can adjust its characteristics based on the driving situation. The personalized driving unit dynamically selects and applies appropriate driving behaviors from the user's profile depending on contextual factors such as road conditions, traffic situations, and environmental factors. This dynamic adaptation allows the system to maintain safety through proven collective patterns while simultaneously customizing the driving style to match individual user preferences and habits.
Data Source
AI summary
An automated system for controlling a vehicle gathers a set of sensor information for a series of driving actions that comprises a driving path of an individual driver. A context is determined that is related to the set of sensor information for the series of driving actions that comprise the driving path of the individual driver. For example, the context may be a weather condition or when the driver merges into traffic. A personalization score is determined for the series of driving actions for the individual driver based on the context. A personalization score is determined for one or more other driving paths and a driving path with the highest personalization score is identified. The driving path with the highest personalization score is then chosen for automated driving.