Route Planning for Semi-Autonomous Vehicles
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Solution Overview
Problem
Conventional navigation systems fail to optimize routes for semi-autonomous and fully autonomous vehicles, as they do not account for autonomous driving capabilities and require user interaction, limiting the ability to plan and calculate routes that can be driven autonomously.
Innovation Solution
A system and method that uses data from various sources to assign a 'percent autonomous' value to route segments, optimizing routes to maximize autonomous driving capability, allowing semi-autonomous or fully autonomous vehicles to plan and navigate to destinations without human input by aggregating data and calculating optimal routes based on distance and autonomous value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional navigation systems are used to plan routes, then users can receive route guidance, but the systems cannot optimize routes for autonomous driving capabilities and require continuous user interaction
Solution Approach 1:
The navigation system automatically evaluates route segments for autonomous driving suitability and selects optimal routes without requiring user input about autonomous driving preferences. The system serves itself by autonomously making routing decisions based on predefined criteria for autonomous vehicle compatibility.
Solution Approach 2:
The system introduces a new parameter 'percent autonomous value' to characterize route segments based on autonomous driving capability. By changing the optimization parameter from traditional distance/time to include autonomous driving suitability, the system enables automated route planning for autonomous vehicles.
2Productivity
If routes are optimized for shortest distance or time, then travel efficiency is improved, but autonomous driving capability is not considered in the optimization
Solution Approach 1:
The routing optimization combines multiple factors into a composite evaluation metric. Instead of optimizing for a single parameter like distance or time, the system creates a composite route evaluation that integrates travel efficiency metrics with autonomous driving capability metrics, similar to how composite materials combine different properties.
Solution Approach 2:
The system adds a new dimension to route optimization by evaluating routes not just in terms of distance and time, but also in terms of autonomous driving suitability. This transforms the optimization problem from two-dimensional (distance-time tradeoff) to three-dimensional by incorporating the autonomous capability dimension.
3Adaptability or versatility
If the system calculates multiple route options with different autonomous values, then user choice is improved, but calculation complexity increases
Solution Approach 1:
The system segments the route into discrete route segments and evaluates each segment's autonomous driving suitability independently. By breaking down the overall route into manageable segments and assigning percent autonomous values to each, the system simplifies the calculation while providing comprehensive route options.
Data Source
AI summary
A method and system is provided for a semi-autonomous vehicle to have optional routes that vary in an amount of autonomous driving offered. The system performs processes including aggregating data from various sources to thereby position and orient the vehicle, assigning an autonomous driving value to different route segments, determining an optimal route by minimizing a distance between a start position and a destination position and maximizing the percent autonomous value assigned, and displaying the optimal route.