Vehicle Navigation Using Operator Intent and Waypoint Priorities
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Solution Overview
Problem
Existing navigation systems for autonomous vehicles do not effectively utilize natural language inputs to align routes with operator intent, prioritize points of interest, or maximize exposure to external advertisement surfaces, failing to consider nuanced user preferences and secondary goals such as scenic routes or advertisement revenue.
Innovation Solution
A navigation application that parses natural language inputs to determine waypoints and candidate routes, considering user profiles and preferences, and optimizes routes to pass by a maximum number of waypoints or advertisement surfaces while adhering to constraints, using sentiment analysis and user profile data to inform route selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the navigation system uses traditional route determination methods, then the route calculation is simple and fast, but it cannot understand natural language inputs and personalize routes based on user intent
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the route determination system. This intermediary parses user intent from natural language inputs, extracts key parameters (such as preferred route types, points of interest, constraints), and translates them into structured queries for the route calculation engine. This allows the system to handle complex, personalized routing requests without requiring complete rewriting of the underlying navigation infrastructure.
2Productivity
If the system prioritizes shortest time or least energy routes, then transportation efficiency is maximized, but user enjoyment and scenic value are sacrificed
Solution Approach 1:
The patent implements a dynamic route optimization system that adjusts routing priorities based on real-time user preferences, historical data, and contextual factors. Instead of fixed optimization criteria, the system dynamically weights multiple objectives (travel time, energy consumption, scenic value, user enjoyment) according to the specific trip context and user profile. This allows the same navigation system to efficiently compute both fastest routes and most enjoyable routes by adjusting the optimization parameters rather than using different algorithms.
Solution Approach 2:
The system changes the parameters used in route calculation based on user intent. When users prioritize efficiency, the system optimizes for minimal time or energy. When users seek enjoyment or scenic routes, the system adjusts parameters to weight points of interest, route characteristics, and user preferences more heavily. This parameter flexibility allows a single system to satisfy conflicting objectives without requiring separate specialized systems.
3Measurement precision
If the navigation system collects and processes extensive user data for personalization, then route personalization accuracy improves, but data privacy concerns and processing complexity increase
Solution Approach 1:
The patent extracts only the essential user preference parameters needed for route determination from extensive user data. Rather than processing all available user information, the system identifies and extracts key attributes (such as preferred route types, favorite points of interest, tolerance for detours, scenic preferences) that directly impact routing decisions. This selective extraction reduces data processing complexity while maintaining personalization accuracy by focusing on the most relevant user characteristics.
4Productivity
If fleet operators minimize transportation costs, then operational efficiency improves, but revenue opportunities from advertisement exposure are reduced
Solution Approach 1:
The patent implements a dynamic fleet routing system that adjusts route selection based on real-time fleet objectives. When cost minimization is the primary goal, the system optimizes for fuel efficiency and direct routes. When advertisement exposure or revenue generation becomes the priority, the system dynamically adjusts to select routes that maximize visibility of advertising surfaces, pass by high-value locations, or optimize delivery timing. This dynamic adjustment allows fleet operators to flexibly shift between operational efficiency and revenue generation based on current business priorities.
Data Source
AI summary
Systems and methods for enabling route determination to maximize an operator's intent are described. In an example, a method includes receiving an input via a user interface, the input comprising a destination. The method also includes determining a primary goal and a secondary goal, wherein the primary goal comprises identifying a traversable route from a starting location to the destination, determining a plurality of waypoints based on the secondary goal, and determining a plurality of candidate routes based on the starting location and the destination. The method then includes identifying, from among the plurality of candidate routes, based on a number of waypoints of the plurality of waypoints positioned along each respective route of the plurality of candidate routes, a target route, and generating for navigation by a vehicle, the target route.