Context-Based Vehicle Interaction via Entity Device Messaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems lack effective mechanisms for context-based interaction between vehicles and entity devices, limiting the ability to autonomously manage vehicle operations based on real-time conditions and user preferences.
Innovation Solution
A system comprising a processor, memory, and components for identifying context conditions, sending context-based messages, and initiating vehicle operations based on instructions from entity devices, utilizing AI and machine learning to enhance interaction and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If context-based interaction systems are implemented between vehicles and entity devices, then user experience and operational convenience are improved, but device complexity and system integration requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: a context identification component that detects conditions, a message component that communicates context-based information, and a control component that executes operations. This segmentation allows each module to be developed, tested, and maintained independently while working together to provide seamless context-based vehicle control
Solution Approach 2:
An intermediary computing system acts as a mediator between the vehicle and external entity devices (such as smartphones or wearables). This intermediary receives context information, processes it according to predefined rules or AI models, and generates appropriate control commands, thereby simplifying the interaction complexity between the vehicle and diverse external devices
2Adaptability or versatility
If AI and machine learning are utilized to enhance interaction and automation, then user experience and contextual awareness are improved, but computational requirements and energy consumption increase
Solution Approach 1:
The system implements AI and machine learning selectively rather than continuously. Context identification uses lightweight algorithms that run constantly, while more computationally intensive AI processing is triggered only when specific context conditions are detected or when complex decision-making is required, thereby reducing overall energy consumption while maintaining high contextual awareness
Solution Approach 2:
Context conditions and user preferences are pre-configured and stored in the system before actual vehicle operations. The AI model is trained offline with historical data, and context-based rules are established in advance. This preliminary preparation reduces the computational burden during real-time vehicle operation, lowering energy consumption while maintaining adaptability
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate context based interaction between a vehicle and an entity device are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a context component that identifies a context condition corresponding to a vehicle or an entity device communicatively coupled to the vehicle. The computer executable components can further comprise a message component that sends a context based message to the entity device based on the context condition. The computer executable components can further comprise a control component that initiates an operation associated with the vehicle based on an instruction input to the entity device in response to the context based message.