Adaptive ADAS Steering Torque for Driver Skill-Based Control
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Solution Overview
Problem
Existing advanced driver assistance systems (ADAS) fail to adapt to differences in driving skills between drivers, leading to inconsistent assistance intensity and reliance by novice drivers, which hinders skill improvement and safety in handling dangerous conditions.
Innovation Solution
A self-adaptive guided ADAS that includes a driving skill classification module, skill learning range classification module, and self-adaptive guided driving right allocation module, using vehicle stability margins and driver states to classify driving skills and allocate driving rights, generating assisted steering torque based on these classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standardized assisted driving is provided, then system complexity is reduced, but driver comfort and willingness to use ADAS deteriorate
Solution Approach 1:
The system dynamically adjusts the level of assistance provided by the ADAS based on the driver's skill level classification. Novice drivers receive more aggressive assistance with higher machine driving right allocation, while experienced drivers receive lighter assistance. This dynamic adaptation resolves the contradiction by making the system complexity manageable while maintaining driver comfort through personalized assistance levels.
Solution Approach 2:
The system applies different assistance characteristics to different driver groups based on their skill levels. Instead of a uniform assistance approach, the system tailors the assistance intensity and type to local conditions - providing more guidance to novice drivers and less to experienced drivers, thereby improving overall driver comfort while maintaining system manageability.
2Reliability
If aggressive assistance is provided to novice drivers, then safety is improved, but driver dependence increases and skill improvement is hindered
Solution Approach 1:
The system implements periodic reassessment of driver skill levels through continuous classification. As drivers gain experience, their skill levels are re-evaluated and adjusted over time. This periodic action allows the system to gradually reduce assistance intensity, promoting skill improvement while maintaining safety through ongoing monitoring and adaptation.
Solution Approach 2:
The assistance intensity is dynamically adjusted based on the driver's current skill level classification. The system provides aggressive assistance when novice drivers need it most for safety, but as drivers improve, the assistance is automatically reduced, allowing skill development while maintaining safety net support when needed.
3Device complexity
If driving right allocation is based solely on deviation from safe area, then control permission is simplified, but driver individuality and skill differences are ignored
Solution Approach 1:
The system segments drivers into different skill level categories (novice, intermediate, experienced) based on their driving behavior patterns. This segmentation allows the system to apply different driving right allocation strategies to different driver groups, considering individual skill differences while maintaining a structured approach to control permission management.
Solution Approach 2:
The system changes the parameter of driver skill level classification to determine driving right allocation. By introducing this new parameter that captures driver individuality and skill differences, the system moves beyond simple deviation-based allocation to a more nuanced approach that considers the driver's experience and capabilities, thereby improving adaptability without excessive complexity.
4Ease of operation
If visual guidance is provided to reduce burden, then driver concentration is improved, but skill learning opportunity is reduced
Solution Approach 1:
The system applies visual guidance selectively based on the driver's skill level and the specific driving situation. For novice drivers in complex situations, visual guidance is provided to maintain concentration and safety. However, the guidance is calibrated to provide just enough support without completely taking over, leaving room for skill learning. The local quality of assistance varies by driver group and situation, balancing concentration support with skill development opportunities.
Data Source
AI summary
The present disclosure relates to a self-adaptive guided advanced driver assistance system (ADAS) considering a driving skill difference between drivers, including a driving skill classification module, configured to calculate a vehicle stability margin based on a vehicle state, and obtain a corresponding driving skill classification result with the vehicle stability margin and a driver state as inputs of a driving skill classification model; a skill learning range classification module, configured to obtain the vehicle stability margin and a distance between a vehicle and a lane line boundary, and use a skill learning range classification model to obtain a skill learning range classification result; and a self-adaptive guided driving right allocation module, configured to realize driving right allocation control based on the driving skill classification result and the skill learning range classification result, and generate an assisted steering torque acting on a vehicle steering system.