Group-Based Navigation Using RSSI Occupant Localization
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Solution Overview
Problem
Existing navigation systems for vehicles do not account for the preferences and parameters of the occupants, leading to inefficient route planning that may not prioritize scenic paths or other group-specific preferences.
Innovation Solution
A method and system that use Received Signal Strength Indication (RSSI) from mobile devices to digitally identify the operator and occupants, evaluate parameters such as prior paths, navigational preferences, and health metrics, and determine vehicle paths using machine learning and neural networks to provide group-based navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional navigation systems are used, then the system is simple and easy to operate, but the route planning does not account for occupant preferences leading to inefficient routing
Solution Approach 1:
The system segments the navigation task by first identifying and characterizing individual occupants (driver, passenger, rear-seat occupants) and then forming groups based on their shared preferences. This segmentation allows the system to process complex occupant data in manageable units and apply different routing strategies to different groups, resolving the contradiction between improved route planning efficiency and system complexity.
Solution Approach 2:
The system performs preliminary actions by pre-characterizing occupants using mobile device data (RSSI signals) and pre-establishing group identities before route planning begins. This preliminary identification and characterization of occupants and their preferences allows the navigation system to efficiently generate personalized routes without requiring complex real-time decision-making, thus improving productivity while managing complexity.
2Adaptability or versatility
If group-based navigation with occupant identification is implemented, then personalized route planning is achieved, but the complexity of identifying and evaluating occupant parameters increases
Solution Approach 1:
The system uses mobile devices as intermediaries to indirectly identify and characterize occupants. Instead of directly monitoring occupants through complex sensors, the system uses RSSI signals from mobile devices already in the occupants' possession as a mediator to infer occupant identity, location, and preferences. This intermediary approach enables personalized navigation while avoiding the complexity of direct occupant monitoring systems.
Solution Approach 2:
The system creates a digital copy or representation of each occupant's preferences and characteristics through characterization data derived from mobile device signals. This copying approach allows the system to store and process occupant preferences as data structures rather than directly managing complex occupant models, thereby achieving high adaptability and personalization while managing computational complexity through data abstraction.
3Measurement precision
If multiple parameters such as prior paths, navigational preferences, and health metrics are evaluated, then more accurate group-based routing is achieved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary evaluation of occupant parameters (prior paths, navigational preferences, health metrics) and stores this characterization data before actual route planning is needed. By pre-processing and storing this information, the system can quickly retrieve and apply relevant parameters during route determination without performing complex evaluations in real-time, thus achieving high measurement precision while minimizing processing time during the actual navigation task.
Solution Approach 2:
The system segments the evaluation of multiple parameters by organizing them into distinct occupant characteristics (location, preferences, health metrics) and evaluating each segment independently. This segmentation allows the system to process and store different parameter types using different methods, then combine them efficiently during route planning, thereby achieving precise multi-parameter optimization while reducing overall processing time through parallel evaluation of segmented parameters.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables more efficient and personalized route planning that considers the preferences and needs of the vehicle occupants, improving the driving experience by optimizing routes based on scenic paths, traffic, fuel efficiency, and stress reduction.
Implementation Method 1
automatically determines a group identity of the operator and the one or more occupants by digitally identifying the operator and the one or more occupants based upon Received Signal Strength Indication (RSSI) of a wireless network with multiple antennas in the vehicle to estimate localization of wireless devices of the operator and the one or more occupants
Data Source
AI summary
A method and system for navigating a vehicle based upon data of a group includes automatically determining a specific group identity for an operator and one or more occupants of a vehicle by digitally identifying the operator and the one or more occupants of the vehicle based upon Received Signal Strength Indication (RSSI) of a wireless network with multiple antennas in the vehicle to estimate localization of wireless devices of the operator and the one or more occupants, evaluating parameters associated with the specific group identity, and determining a path for the vehicle based upon the specific group identity.