In-Vehicle Function Recommendation Using Context-Aware Driver Assistance
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Solution Overview
Problem
Existing vehicle function recommendations are often based on static criteria, leading to inadequate usage rates and potential driver distraction due to inappropriate recommendations.
Innovation Solution
A computer-implemented method using a recommendation model, such as an artificial neural network, to provide context-specific function recommendations based on real-time context information, including geographical location and environmental parameters, to enhance the likelihood of a supportive driving experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static requirements are used for function recommendation, then the system guarantees proper operation and avoids inadequate recommendations, but the recommendation accuracy and driver satisfaction decrease
Solution Approach 1:
The patent transforms the recommendation system from using static, fixed parameters to dynamic parameters that continuously adapt based on learned driver preferences and contextual information. The recommendation model learns optimal parameter settings from historical data, enabling it to provide context-specific recommendations that improve accuracy while maintaining reliability through learned patterns rather than rigid rules.
Solution Approach 2:
The system employs machine learning models that automatically learn and improve recommendation accuracy without manual intervention. The model self-adjusts by processing contextual data and driver responses, eliminating the need for developers to manually configure static requirements for every possible driving scenario, thereby improving both accuracy and adaptability.
2Reliability
If multiple ADAS functions are provided in modern vehicles, then driving safety and comfort are enhanced, but drivers become overwhelmed and underutilize these functions
Solution Approach 1:
The patent introduces a recommendation system as an intermediary between the driver and the multiple ADAS functions. This intermediary processes contextual information about the driving situation and driver preferences, then selectively recommends only the most relevant functions, reducing the cognitive burden on drivers while ensuring safety-critical functions are appropriately suggested.
Solution Approach 2:
The system applies local quality by providing customized recommendations tailored to specific driving contexts and individual driver preferences rather than presenting all functions uniformly. The recommendation model adapts to local conditions (specific driving scenarios) and local user characteristics (driver preferences), making the system more usable while maintaining comprehensive safety coverage.
3Adaptability or versatility
If drivers manually activate ADAS functions, then function availability is ensured, but activation rates remain low due to driver forgetfulness and complexity
Solution Approach 1:
The system performs preliminary action by proactively recommending functions before drivers need them, based on predicted driver needs and current contextual conditions. The recommendation model analyzes upcoming driving scenarios and suggests functions in advance, reducing driver forgetfulness and improving activation rates while maintaining function availability when needed.
Solution Approach 2:
The system implements feedback loops where driver responses to recommendations (activation or rejection) are fed back into the model to improve future recommendations. This continuous learning process increases function usage rates by adapting to actual driver behavior patterns while ensuring that relevant functions remain available through iterative improvement of recommendation accuracy.
Data Source
Figure 1A~1B
Figure 2
Figure 3A~3B
AI summary
The present invention relates to function recommendation in a vehicle. In particular, the present invention relates to a computer-implemented method for providing a function recommendation in a vehicle (100). The method comprises the following steps: - loading a recommendation model (RM); - receiving context information (C) acquired by using at least one sensor (111) of the vehicle (100), the context information (C) in particular comprising a geographical location of the vehicle; - determining a function (F), in particular a vehicle function, using the context information (C) and the recommendation model (RM); - providing a function recommendation associated with the function (F), in particular using a display (112) and/or a voice assistant of the vehicle (100).