Autonomous Vehicle Driving Profile Generation
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Solution Overview
Problem
Existing autonomous vehicles lack the ability to personalize driving styles to accommodate individual user preferences, leading to compatibility issues with different users, as they often rely on fixed driving modes that do not adapt to varying user behaviors and preferences.
Innovation Solution
A computer-implemented method that collects driving statistics and user-specific information to generate personalized driving profiles, which are used to control the autonomous vehicle, emulating human driving behaviors and preferences by communicating with sensors and remote servers to determine optimal driving styles and routes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed driving modes are used in autonomous vehicles, then the system complexity is reduced and ease of operation is improved, but the adaptability to individual user preferences deteriorates
Solution Approach 1:
The patent implements dynamic driving styles that automatically adapt to individual users based on their behavior patterns and preferences. The system transitions from static fixed modes to dynamic personalized modes by monitoring user interactions and adjusting driving parameters in real-time, resolving the contradiction between ease of operation and adaptability.
Solution Approach 2:
The system performs self-learning by automatically observing and analyzing user driving behaviors without requiring explicit user input or configuration. The autonomous vehicle autonomously generates personalized driving profiles by processing sensor data and user interactions, eliminating the need for manual mode selection while providing customized driving experiences.
2Adaptability or versatility
If personalized driving profiles are generated through data collection and machine learning, then the adaptability to user preferences is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent employs a unified machine learning framework that handles multiple functions including behavior analysis, preference learning, and driving parameter optimization within a single system architecture. This multi-functional approach reduces overall system complexity compared to implementing separate specialized systems for each function.
Solution Approach 2:
The system introduces an intermediary learning layer that processes raw sensor data and user interactions, transforming them into standardized driving profiles that can be applied across different driving scenarios. This intermediary layer simplifies the complexity by creating a standardized interface between data collection and driving control systems.
Data Source
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AI summary
Driving statistics of an autonomous vehicle are collected. The driving statistics include driving commands issued at different points in time and route selection information of one or more routes while the autonomous vehicle was driven in a manual driving mode by one or more users. For each user of the autonomous vehicle, one or more user driving behaviors and preferences of the user are determined from at least the driving statistics for predetermined driving scenarios. One or more driving profiles for the user are generated based on the determined user behaviors and preferences under the driving scenarios, where the driving profiles are utilized to control the autonomous vehicle under similar driving scenarios when the user is riding in the autonomous vehicle that operates in an autonomous driving mode.