Adaptive Driver Mentoring Control for Skill-Aware Vehicle Intervention
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Solution Overview
Problem
Existing driver assistance systems in vehicles primarily focus on intervening in critical situations rather than improving driver skills over time, failing to adapt to the driver's developing abilities and thus limiting long-term training effectiveness.
Innovation Solution
A mentoring device that continuously adapts its interventions based on the driver's current abilities by determining deviations from a target course, using an active support profile to reduce these deviations, and adjusting its behavior based on an acceptance value derived from past deviations, allowing for variable intervention intensity and personalized support profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driver assistance systems intervene in critical situations, then vehicle safety is improved, but driver skill development is limited
Solution Approach 1:
The system dynamically adjusts the level of intervention based on the driver's current skill level and performance. As the driver improves, the system reduces intervention intensity, allowing the driver to develop skills independently while maintaining safety. This dynamic adaptation resolves the contradiction by making the system both safe (through intervention when needed) and adaptive to driver development (by reducing intervention as skills improve).
Solution Approach 2:
The system changes key parameters such as intervention threshold, support intensity, and monitoring frequency based on the driver's progression. By adjusting these parameters over time, the system maintains safety while progressively enabling greater driver autonomy and skill development, thus resolving the contradiction between safety intervention and skill development.
2Device complexity
If driver assistance systems provide fixed intervention protocols, then system complexity is reduced, but training effectiveness decreases
Solution Approach 1:
The system transitions from fixed intervention protocols to dynamic, adaptive protocols that adjust in real-time based on driver performance. This allows the system to maintain relatively simple architecture while achieving high training effectiveness through continuous adaptation to the individual driver's needs and progression.
Solution Approach 2:
The system incorporates continuous feedback loops that monitor driver performance and adjust intervention strategies accordingly. This feedback mechanism enables the system to maintain effectiveness by adapting to driver development while avoiding the complexity of pre-programming all possible intervention scenarios, thus resolving the contradiction between simplicity and effectiveness.
3Speed
If mentoring device intervenes frequently to reduce deviations, then driver skill improvement is accelerated, but driver acceptance decreases
Solution Approach 1:
The system dynamically adjusts intervention frequency and intensity based on the driver's acceptance level and performance needs. When acceptance is high, more frequent interventions are provided to accelerate skill improvement. When acceptance decreases, the system reduces intervention intensity, allowing the driver to maintain autonomy. This dynamic balancing resolves the contradiction by adapting intervention strategies to both skill development needs and driver acceptance.
Solution Approach 2:
The system changes parameters such as intervention threshold, support type, and feedback frequency based on driver acceptance and performance metrics. By adjusting these parameters, the system optimizes the balance between accelerating skill improvement and maintaining driver acceptance, thus resolving the contradiction between speed of improvement and ease of operation.
Data Source
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AI summary
The invention relates to a mentoring device (1) for assisting a user (2) in the handling of a predefined object (3), the mentoring device (1) being designed to receive a target course (4) of the handling and to determine a current deviation (5) of an actual course (6) of the handling from the target course (4), and, in accordance with an active profile of support (7) by the mentoring device (1), to carry out an intervention (8) in the handling in order to reduce the deviation (5). According to the invention, the mentoring device (1) is designed to determine an acceptance value (9), which describes deviation behavior (10) of the user (2) with regard to past deviations (11) from the target course (4) over a predefined time period, and, in dependence on the acceptance value (9), to bring the target course (4) closer to an expected further actual course (12) determined on the basis of the deviation behavior (10), and/or to select one of several profiles of support (14) as the active profile of support (7) in dependence on the acceptance value (9).