Autonomous Vehicle Canonical Routing With Post-Boarding Adjustments
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Solution Overview
Problem
Autonomous vehicles face challenges in dynamically adjusting routes based on user preferences and real-time conditions, often resulting in non-customized or non-optimal travel paths.
Innovation Solution
A method and system that determine a first canonical route based on user request data and subsequently adjust to a second canonical route upon user input or boarding, using a computer system to process route identification data and provide route data for controlling the autonomous vehicle's travel, incorporating route preference and current condition data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a fixed canonical route is determined before user boarding, then route determination efficiency is improved, but route adaptability to user preferences deteriorates
Solution Approach 1:
The system implements dynamic route adjustment by allowing the canonical route to change from a first route determined before user boarding to a second route after user boarding and preference input. The route determination is no longer static but adapts dynamically based on user feedback, real-time conditions, and preferences while maintaining efficiency through the canonical route framework.
Solution Approach 2:
The system incorporates user feedback mechanisms where user preferences and inputs received after boarding are fed back into the route determination process. This feedback loop enables the system to adjust the canonical route from the first route to a second route that better satisfies user preferences while maintaining operational efficiency.
2Adaptability or versatility
If route adjustments are made in real-time based on user input, then route adaptability is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary route determination by establishing a first canonical route before user boarding. This preliminary action provides a baseline route that can be efficiently determined in advance, reducing the complexity of real-time adjustments while maintaining adaptability through subsequent modifications based on user input.
Solution Approach 2:
The route determination process is segmented into distinct phases: initial canonical route determination before boarding, user preference collection, and route adjustment after boarding. This segmentation allows each phase to be handled with appropriate complexity levels, maintaining overall system manageability while achieving high adaptability.
3Manufacturing precision
If multiple canonical routes are evaluated, then route optimization quality is improved, but processing time increases
Solution Approach 1:
The system optimizes the balance between evaluation thoroughness and processing time by dynamically adjusting parameters such as the number of canonical routes evaluated, the depth of condition analysis, and the timing of detailed evaluations. This allows high-quality route optimization when time permits while enabling faster determination when needed.
Solution Approach 2:
The system employs partial evaluation of multiple canonical routes by assessing key criteria for all routes and performing detailed analysis only for the most promising candidates. This partial action approach maintains high optimization quality by thoroughly evaluating the best options while reducing overall processing time by not exhaustively analyzing all possible routes.
Data Source
AI summary
A method for determining and providing alternative routes receives request data associated with a request from a user device. A first canonical route is determined from a plurality of canonical routes based on the request data. Each respective canonical route of the plurality of canonical routes satisfies at least one autonomy criteria associated with whether an autonomous vehicle can travel on the respective canonical route. First route data associated with the first canonical route is provided. Route identification data associated with identifying an alternative canonical route is received after providing the first route data associated with the first canonical route. A second canonical route is determined from the plurality of canonical routes based on the route identification data. Second route data associated with the second canonical route is provided for controlling travel of the autonomous vehicle on the second canonical route.