Autonomous Vehicle Route Sharing for Socially Linked Pickups
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Solution Overview
Problem
Existing routing systems for autonomously driven passenger vehicles are limited in their ability to dynamically adjust routes based on user preferences and associations, often only suggesting a few locations such as home or work, without considering social connections or group preferences.
Innovation Solution
A method and system that generates routes for autonomous vehicles to transport multiple passengers by determining proximity, association, and destination criteria, allowing for the presentation of ride-sharing opportunities through user interfaces, and adjusting routes to include additional passengers en route to a shared destination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the routing system only suggests a limited number of locations (home or work) based on historical data, then the system complexity is reduced and operation is simplified, but the adaptability to user preferences and social associations is limited
Solution Approach 1:
The routing system is segmented into multiple independent modules: a historical data analysis module that processes past travel patterns, a social association module that identifies connected users and their preferences, a route generation module that creates multiple route options, and a user interface module that presents choices. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while improving adaptability to user preferences and social associations.
Solution Approach 2:
The routing system is designed with multi-functionality to handle diverse user needs. It can suggest routes based on historical data, social associations, real-time traffic conditions, and user preferences simultaneously. The system universally serves multiple purposes: providing navigation, recommending destinations, facilitating carpooling opportunities, and adapting to individual user behaviors, thereby improving versatility without proportionally increasing complexity.
2Productivity
If the routing system dynamically adjusts routes based on multiple user associations and preferences, then service efficiency and passenger convenience are improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing route options, user preferences, and social association data before they are needed. Historical travel patterns are analyzed in advance, and potential carpooling opportunities are pre-identified. When a user requests routing information, the system quickly retrieves and presents preprocessed data rather than computing everything in real-time, thereby improving service efficiency while managing computational complexity.
Solution Approach 2:
The routing system implements dynamics by allowing real-time adjustment of routes based on changing conditions. The system dynamically recalculates routes when new information becomes available, such as traffic conditions, user availability, or changing destinations. This dynamic capability enables the system to adapt to current situations, improving productivity and passenger convenience while the modular architecture manages computational complexity through selective recalculation rather than complete reprocessing.
3Productivity
If the system presents ride-sharing opportunities to multiple users based on proximity and association criteria, then passenger utilization and route efficiency are enhanced, but the communication overhead and user interface complexity increase
Solution Approach 1:
The system extracts and separates the ride-sharing opportunity presentation from the main routing function. When identifying potential carpooling opportunities based on proximity and association criteria, the system extracts this information and presents it as a separate, optional feature to users. This extraction allows the core routing functionality to remain simple and easy to operate, while the ride-sharing feature can be explored independently, thereby enhancing passenger utilization without significantly increasing user interface complexity.
Solution Approach 2:
The system introduces an intermediary layer that acts as a mediator between the complex routing calculations and the user interface. This intermediary processes ride-sharing opportunities, filters relevant information, and presents it in a simplified manner to users. The intermediary handles the communication overhead by pre-processing and organizing data before presentation, reducing the complexity perceived by users while maintaining high passenger utilization through effective ride-sharing recommendations.
Data Source
AI summary
A method, autonomous system controller, and computer program product generate a first route for an autonomous vehicle to transport a first person from a first location to a destination. In response to determining that a second person satisfies trigger criteria comprising: (i) being at a second location that is within a proximity threshold to the first route; (ii) being associated with the first person; and (iii) being associated with the destination, an affordance is caused to be presented via respective user interface devices to at least one of the first and second persons that proposes that the autonomous vehicle transport both the first and second persons to the destination. In response to receiving acceptance, a second route is generated for the autonomous vehicle that comprises picking up the first person at the first location, picking up the second person at the second location, and transporting both persons to the destination.