Multi-Modal AV Route Coordination for Dynamic Pickup Updates
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Solution Overview
Problem
Conventional transportation systems require users to manually determine and arrange multiple transportation modalities, which can be time-consuming and costly, especially during high traffic conditions, and do not efficiently coordinate autonomous vehicles (AVs) to adapt to changes in user routes.
Innovation Solution
A multiple-modality transportation system that includes a client computing device, a server computing device, and AVs, which optimizes routes considering various transportation modalities such as AVs, trains, buses, and walking, and dynamically updates the route plan in real-time to ensure efficient and cost-effective travel by coordinating AVs to meet users at updated locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually determine and arrange multiple transportation modalities, then they can potentially optimize their travel route, but the process becomes time-consuming and complex
Solution Approach 1:
The system performs automatic multi-modality route optimization without requiring user intervention. The server computing device autonomously determines optimal routes combining multiple transportation modalities (AVs, trains, buses, walking) based on real-time data, eliminating the time users would spend manually planning while maintaining versatile route optimization capabilities
Solution Approach 2:
The transportation system integrates multiple transportation modalities (autonomous vehicles, public transit, walking routes) into a single unified platform. The server computing device handles route optimization across all these different modalities simultaneously, providing a universal solution that replaces the need for users to separately arrange each transportation type
2Ease of operation
If users manually arrange each transportation modality individually, then they can make specific arrangements, but the process eliminates potential time and cost savings
Solution Approach 1:
The system merges multiple individual transportation arrangements into a single integrated multi-modality route plan. The server computing device combines AV pickup, public transit connections, and walking segments into one coordinated itinerary that users can obtain through a single request, maintaining ease of operation while dramatically improving efficiency by eliminating the need to separately arrange each leg of the journey
3Productivity
If the system coordinates AVs to adapt to user route changes, then travel efficiency is improved, but the system complexity increases
Solution Approach 1:
The system implements real-time feedback mechanisms where the server computing device continuously monitors user location, transportation status, and route conditions. When changes occur (such as user deviation from planned route or transportation delays), the system automatically receives feedback and recalculates optimal routes, coordinating AVs accordingly. This feedback loop improves travel efficiency while managing system complexity through automated real-time adjustments rather than complex pre-planning
Data Source
AI summary
Technologies disclosed herein facilitate identification of a trip route from an origin of a user to a desired destination of the user, wherein the trip route includes multiple transportation modalities, at least one of which is an autonomous vehicle (AV), and dispatching of the AV to a pickup location for the user in connection with providing transportation to the user along a portion of the identified trip route.