Autonomous Vehicle Navigation System With Dynamic Route Optimization
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Solution Overview
Problem
Conventional navigation systems for autonomous vehicles do not account for driver preferences and enhanced capabilities such as hands-free time, sleep time, and road safety, which are crucial for optimized routing in autonomous driving scenarios.
Innovation Solution
A navigation apparatus and method that allows users to input control parameters and weights, which are then processed by a server using an optimization algorithm to generate and display optimum routes for autonomous vehicles, considering factors like travel time, hands-free time, sleep time, road safety, and fuel economy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional navigation systems are used for autonomous vehicles, then basic routing functionality is provided, but driver preferences and autonomous-specific factors (hands-free time, sleep time, road safety) are not accounted for
Solution Approach 1:
The navigation system is segmented into multiple independent modules: a control parameter receiving module that accepts user inputs, a weight receiving module that prioritizes parameters, an optimization algorithm module that processes routing, and a route displaying module that presents results. This segmentation allows the system to handle complex autonomous vehicle requirements while maintaining manageable system architecture.
Solution Approach 2:
The navigation apparatus is designed with multi-functionality to serve both conventional routing needs and autonomous vehicle-specific requirements. It can simultaneously consider traditional factors (travel time, distance) and autonomous factors (hands-free time, sleep time, road safety), making it universally applicable to diverse routing scenarios without requiring separate systems.
2Measurement precision
If multiple control parameters and weights are integrated into route optimization, then routing precision is improved, but computational complexity increases
Solution Approach 1:
The optimization algorithm acts as an intermediary between multiple control parameters (with their respective weights) and the final route determination. It receives weighted parameters from the user, processes them through systematic optimization steps, and produces an optimized route. This intermediary structure manages computational complexity by breaking down the optimization process into discrete, manageable operations.
Solution Approach 2:
The system allows dynamic adjustment of control parameters and their weights, enabling users to change optimization criteria based on specific needs. By parameterizing the optimization problem, the system can adapt to different scenarios (e.g., prioritizing road safety over travel time) without requiring fundamental algorithmic changes, thus managing complexity while maintaining precision.
3Measurement precision
If user preferences and historic driving performance are incorporated into route determination, then routing accuracy is improved, but information processing requirements increase
Solution Approach 1:
User preferences and historic driving performance data are collected and stored in advance before the actual routing decision is needed. This preliminary data collection allows the system to quickly reference pre-processed information during route determination, reducing real-time data processing requirements while maintaining high routing accuracy.
Solution Approach 2:
The system incorporates feedback loops where historic driving performance and user preferences are continuously updated based on actual driving outcomes. This feedback mechanism allows the system to learn from past experiences and improve future routing decisions without requiring excessive new data processing, as the system refines its understanding over time with incremental updates.
Data Source
AI summary
A navigation apparatus (including configurable circuitry) and method for displaying a plurality of control parameters to carry out a process for generating at least one optimum route for use in navigation of an autonomous vehicle, receiving a plurality of weights corresponding to the plurality of control parameters to generate a route score corresponding to the at least one route, transmitting the plurality of control parameters and the plurality of weights to a server implementing an optimization algorithm to calculate the at least one optimum route using the plurality of control parameters and the plurality of weights, receiving the at least one optimum route and the route score generated by the server, and displaying the at least one optimum route and the route score to enable selection of the at least one optimum route for navigation of the autonomous vehicle from the start location to the destination.