Autonomous Driving Pattern Learning for Personalized Vehicle Control
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Solution Overview
Problem
Existing autonomous driving technologies cannot learn and adapt to individual user driving patterns, resulting in all users being subjected to the same driving pattern, which may not suit their preferences or driving styles.
Innovation Solution
A driving pattern learning apparatus and method that allows users to select between an autonomous driving mode and a driving pattern learning mode, enabling the system to learn and store user-specific patterns for acceleration, braking, steering, inter-vehicle distance, lane changes, overtaking, and responses to road facilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If autonomous driving technology uses a standardized driving pattern for all users, then the system complexity is reduced and ease of manufacture is improved, but adaptability to individual user preferences deteriorates
Solution Approach 1:
The system performs preliminary learning of user driving patterns during a dedicated learning phase before autonomous driving operation. The driving pattern learning unit collects and analyzes user driving data (acceleration, braking, steering, inter-vehicle distance) during this preliminary stage, so that when autonomous driving mode is activated, the system already has personalized patterns ready to apply, eliminating the need for complex real-time adaptation algorithms
Solution Approach 2:
The system creates simplified copies of user driving patterns by storing key parameters (acceleration patterns, braking patterns, steering patterns, inter-vehicle distance preferences) in a database. These copied patterns are then reused during autonomous driving operations, avoiding the need for complex real-time analysis while maintaining personalization. The memory stores these pattern copies for efficient retrieval and application
2Adaptability or versatility
If the system collects and learns detailed user driving patterns, then adaptability to user preferences is improved, but the time required for data collection and processing increases
Solution Approach 1:
The learning process is segmented into distinct phases: a dedicated driving pattern learning mode where data is collected, and an autonomous driving mode where learned patterns are applied. This segmentation allows the system to concentrate data collection efforts during the learning phase without extending the actual autonomous driving operation time. The control unit manages this temporal segmentation, ensuring that learning activities occur only when appropriate
Solution Approach 2:
The system focuses on learning only the most essential driving pattern elements (acceleration, braking, steering, inter-vehicle distance) rather than attempting to capture every aspect of driving behavior. This partial action approach reduces the total data collection and processing time while still achieving meaningful personalization. The system learns excessive data during the dedicated learning phase to ensure sufficient patterns are captured for various driving scenarios
Data Source
AI summary
An embodiment driving pattern learning apparatus includes a driving mode selection unit configured to receive an autonomous driving mode or a driving pattern learning mode using an input unit, a driving pattern learning unit configured to learn a driving pattern of a user comprising acceleration, braking, steering, an inter-vehicle distance, a lane change, overtaking, or a response to road facilities based on a process of driving by the user in the driving pattern learning mode, and a memory configured to store the driving pattern learned by the driving pattern learning unit.