Activity-Based AV Routing Recommendations for Multi-Stop Exploration
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Solution Overview
Problem
Existing autonomous vehicle (AV) ride-hailing services are limited in providing routing options that allow users to explore multiple locations based on their activities of interest, especially in cases where exact addresses are not known or multiple destinations are involved.
Innovation Solution
A routing recommendation system that utilizes user data, location data, and AI/ML to suggest locations and optimal drop-off points based on user preferences, history, and activities, enabling users to explore areas by recommending locations and generating itineraries tailored to their interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional AV ride-hailing services provide routing options, then users can travel between locations, but users cannot explore multiple locations based on their activities of interest when exact addresses are not known
Solution Approach 1:
The system performs preliminary actions by proactively generating routing recommendations that incorporate user activities of interest before the user needs to make routing decisions. The server automatically identifies locations related to user activities and prepares optimized routing options, so users don't need to manually search for or plan multiple destinations.
Solution Approach 2:
The system enables self-service by allowing users to simply input their activities of interest without needing to specify exact addresses or plan routes manually. The server autonomously processes the activities, generates location recommendations, creates optimized routing options, and presents them to users, who then only need to select their preferred option.
2Adaptability or versatility
If the system generates personalized routing recommendations based on user activities, then user experience is enhanced, but system complexity increases
Solution Approach 1:
The server acts as an intermediary that handles the complex processing between user input and routing generation. It receives activities of interest, automatically identifies related locations using its database, generates multiple routing options with different priorities, and presents them to users. This intermediary approach centralizes complexity in the server rather than requiring complex client-side processing.
Solution Approach 2:
The system segments the routing recommendation process into distinct components: activity input reception, location identification, route generation with different priorities, and recommendation presentation. This segmentation allows each component to be handled independently by the server, managing complexity through modular processing steps.
3Adaptability or versatility
If the system provides multiple routing recommendations with different priorities, then users can choose optimal routes, but processing time increases
Solution Approach 1:
The system applies partial action by generating routing recommendations with different priority levels (first priority, second priority, etc.) rather than creating every possible routing variation. This provides users with sufficient diverse options to choose from while avoiding the exponential time cost of generating all conceivable routes.
Data Source
AI summary
Disclosed are embodiments for facilitating a routing recommendation system based on user activities. In some aspects, an embodiment includes receiving input data comprising a list of activities requested by a user of an autonomous vehicle (AV) ride-hailing service and a point of destination requested by the user; mapping locations of interest corresponding to the list of activities; generating an ordered list of the locations of interest based on a user profile and historical data of the user; supplementing the ordered list of the locations of interest based on other historical data of other users of the AV ride-hailing service to generate a recommended list of places; applying a K-means clustering technique to the recommended list of places to identify one or more sets of recommendations and a centroid location for each set of recommendations; and providing a routing recommendation corresponding to a selected set of the sets of recommendations.