Vehicle Routing System Integrating Driver and Vehicle Data
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Solution Overview
Problem
Current vehicle routing systems lack integration of vehicle-specific and driver-specific data, leading to inefficient navigation decisions that do not account for the vehicle's needs or the driver's preferences, resulting in suboptimal routes that may not consider factors like fuel levels, maintenance requirements, or personal preferences.
Innovation Solution
A system that collects and processes both vehicle and driver data to create personalized routing decisions by using a network architecture that integrates MQTT for data communication, a processing engine for rule evaluation, and AI for predictive analysis, allowing for real-time optimization of routes based on vehicle and driver profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional routing systems use only basic navigation instructions from A to B, then the system complexity remains low, but the routing efficiency and adaptability to vehicle-specific needs deteriorates
Solution Approach 1:
The patent merges vehicle-specific data (fuel level, battery charge, maintenance status) with driver-specific data (preferences, historical behavior) and real-time environmental data into a unified routing decision framework. This integration allows the system to optimize routes by considering multiple factors simultaneously, resolving the contradiction between routing efficiency and system complexity through data consolidation and holistic analysis
2Productivity
If the system collects and processes both vehicle and driver data, then the routing optimization improves, but the data processing complexity and computational requirements worsen
Solution Approach 1:
The patent segments data processing into distinct modules: vehicle data processing, driver data processing, environmental data processing, and route optimization processing. Each module handles specific data types and processing tasks independently, then integrates results through a unified decision framework. This segmentation reduces overall processing complexity while maintaining comprehensive analysis capabilities
3Adaptability or versatility
If real-time vehicle data is used for routing decisions, then the adaptability to vehicle needs improves, but the response time and data collection overhead worsen
Solution Approach 1:
The patent implements preliminary action by continuously collecting and pre-processing vehicle data (fuel level, battery charge, maintenance status) and driver data (preferences, historical routes) before routing decisions are needed. This pre-collection and pre-processing eliminates time delays during actual route planning, as the system already has organized data ready for immediate analysis when a routing decision is required
Data Source
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AI summary
A routing system per the present teaching is configured to collate data from individual users with data from individual vehicles in a way that allows personalised optimization of the routing of the vehicle over and beyond routing that is triggered by one or either of the vehicle or the user. In effect, the use of data from both the user and the vehicle can actively allow for more intelligent navigation decisions that are communicated by the system to at least one of a human machine interface of the operating vehicle or on a mobile device associated with the user of the vehicle.