Autonomous Driving Style Control for Personalized Ride Behavior
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Solution Overview
Problem
Existing automatic driving systems lack the ability to adapt to individual user preferences and driving styles, resulting in a uniform and often unsatisfactory riding experience for passengers.
Innovation Solution
Implementing a control method that allows users to select or have their driving style learned through personalized self-learning, adjusting vehicle control parameters such as lane change, acceleration, deceleration, following distance, and maximum speed based on different driving modes (conservative, conventional, aggressive, personalized) to enhance user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automatic driving control is implemented with fixed parameters, then the system is simple to operate, but the riding experience does not adapt to individual user preferences
Solution Approach 1:
The system automatically learns user driving preferences through self-service mechanisms by monitoring and analyzing driving behaviors without requiring explicit user programming. The control parameters are automatically adjusted based on learned patterns of acceleration, deceleration, lane changing, and following distance preferences, enabling the system to adapt to individual users while maintaining operational simplicity.
Solution Approach 2:
The system performs preliminary actions by pre-learning and storing multiple driving mode templates (conservative, conventional, aggressive, personalized) before actual use. These pre-configured templates allow the system to quickly adapt to user preferences without requiring complex real-time calculations, thus improving adaptability while controlling system complexity.
2Adaptability or versatility
If multiple driving modes are provided, then the system becomes more personalized, but the ease of operation decreases
Solution Approach 1:
The system automatically determines and switches between different driving modes based on learned user preferences and current driving conditions, eliminating the need for manual mode selection by the user. This self-service approach provides multiple personalized driving modes while maintaining ease of operation, as the system handles mode selection autonomously based on accumulated learning data.
3Ease of operation
If driving parameters are adjusted independently, then the control system is simple, but the riding experience lacks coordination across different driving aspects
Solution Approach 1:
The system merges multiple independent driving parameter controls (acceleration, deceleration, lane changing, following distance) into a unified coordinated control framework. By integrating these parameters and applying them together based on selected driving modes and learned user preferences, the system achieves coordinated driving control that enhances riding experience while maintaining reasonable system simplicity through modular architecture.
Data Source
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Figure 3
AI summary
Disclosed are a control method and apparatus, a device and a storage medium. The control method includes: a current automatic driving mode of a vehicle is acquired, wherein the current automatic driving mode includes a conservative style driving mode, a conventional style driving mode, an aggressive style driving mode or a personalized style driving mode, respective driving policies corresponding to the conservative style driving mode, the conventional style driving mode, the aggressive style driving mode and the personalized style driving mode are different, and the driving policies include a lane change policy, an acceleration policy, a deceleration policy, a vehicle following distance policy and a maximum speed policy; and the vehicle is controlled to perform automatic driving according to the driving policy corresponding to the current automatic driving mode.