In-Vehicle Function Recommendation Using Context-Aware AI
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern vehicles' driver-assistance systems (ADAS) face low usage rates due to drivers being unaware of available functions or overwhelmed by numerous options, leading to safety concerns and suboptimal functionality.
Innovation Solution
A computer-implemented method using a recommendation model based on artificial intelligence, such as an artificial neural network, to provide context-specific function recommendations to drivers, leveraging vehicle sensors for real-time data like geographical location and driving conditions, to enhance the usability and safety of ADAS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static requirements are used for function recommendation, then the system complexity is reduced and operation is simplified, but the recommendation accuracy and driver support experience deteriorate
Solution Approach 1:
The system transitions from static pre-determined requirements to dynamic context-aware recommendations. The recommendation model continuously adapts to current driving situations by processing real-time context information from multiple sensors, making the recommendation criteria flexible and situation-specific rather than fixed and rigid.
Solution Approach 2:
The system changes the parameters used for recommendation from fixed static requirements to dynamic context parameters including geographical location, driving behavior patterns, environmental conditions, and vehicle state. This parameter transformation enables more accurate and situation-appropriate recommendations.
2Measurement precision
If context-specific recommendation using AI model is implemented, then recommendation accuracy and driver support experience improve, but the device complexity and computational requirements increase
Solution Approach 1:
The system segments the complex recommendation task into manageable components: context information acquisition from multiple sensors, processing through the recommendation model, and output generation. This modular segmentation reduces overall system complexity by breaking down the AI-driven recommendation process into distinct functional modules.
Solution Approach 2:
The recommendation model acts as an intermediary between raw sensor data and function recommendations. This intermediary layer processes and interprets complex multi-source context information, transforming it into actionable recommendations while shielding the rest of the system from the complexity of raw data processing.
3Loss of information
If multiple sensors are used for context information acquisition, then the comprehensiveness of driving situation characterization improves, but the quantity of data and processing requirements increase
Solution Approach 1:
The recommendation model extracts only the most relevant features and parameters from the abundant sensor data, filtering out redundant information. This extraction process maintains information completeness by focusing on critical context elements while reducing the overall data volume that needs to be processed and stored.
Data Source
AI summary
A computer-implemented method for providing a function recommendation in a vehicle is disclosed herein. The method includes loading a recommendation model, and receiving context information acquired from at least one sensor of the vehicle, the context information in particular comprising a geographical location of the vehicle. The method further includes determining at least one function including a vehicle function using the context information and the recommendation model, and providing a function recommendation associated with the vehicle function using a display or a voice assistant of the vehicle.