Large Venue Pickup Location Mapping for Autonomous Vehicle Access
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Solution Overview
Problem
Existing methods for identifying pickup and drop-off locations at large venues, such as parks and airports, are often manual and inefficient, lacking an automated process to optimize these locations for autonomous vehicles.
Innovation Solution
A method using server computing devices to identify large venue points of interest based on historical trip data and map information, associating pickup and drop-off locations with these points, and providing users with optimized location options for efficient pick-ups and drop-offs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to identify pickup and drop-off locations at large venues, then flexibility in location selection is maintained, but efficiency and optimization for autonomous vehicles deteriorate
Solution Approach 1:
The system automatically identifies pickup and drop-off locations using historical trip data and map information without requiring manual intervention. The processors autonomously analyze data patterns, identify optimal locations, and associate them with large venue points of interest, enabling the system to serve itself rather than relying on manual methods
Solution Approach 2:
The system pre-identifies and stores multiple pickup and drop-off locations for large venues before they are actually needed. By analyzing historical data in advance and creating a database of optimal locations, the system prepares ahead of time so that when a trip is requested, the locations are already identified and ready for use
2Adaptability or versatility
If multiple pickup and drop-off locations are provided for large venues, then user options and experience are improved, but system complexity increases
Solution Approach 1:
The system segments the large venue point of interest into multiple distinct pickup and drop-off locations based on historical trip data and map information. Each location is identified as a separate point with specific coordinates, allowing the system to provide multiple options without treating the entire venue as a single complex entity
Solution Approach 2:
The system uses historical trip data and map information as intermediary data layers to bridge the gap between the large venue point of interest and specific pickup/drop-off locations. These intermediaries simplify the process by providing structured data about where pickups and drop-offs have occurred, eliminating the need for complex real-time analysis
3Loss of time
If automated identification of pickup and drop-off locations is implemented, then travel time and waiting time are reduced, but data processing requirements increase
Solution Approach 1:
The system performs data processing in advance by analyzing historical trip data and identifying optimal pickup and drop-off locations before they are actually needed. This preliminary processing creates a ready-to-use database of locations, so when trips occur, no additional real-time processing is required, reducing both time loss and peak energy consumption
Solution Approach 2:
The system creates a simplified representation or copy of the optimal pickup and drop-off locations based on historical data patterns. Instead of processing complex real-time data for each trip, the system uses pre-generated location copies that capture the essential information needed for efficient autonomous vehicle operation
Data Source
AI summary
Aspects of the disclosure provide for generating and using a model for identifying a plurality of pick up or drop off locations for a large venue point of interest. For instance, a large venue point of interest may be identified. The plurality may be identified for the large venue point of interest based on a combination of historical trip data and map information. The plurality may be associated with the large venue point of interest. The association may be stored in memory for later use. Thereafter, a request for a trip identifying the large venue point of interest may be received from a client computing device, and the plurality may be provided to the client computing device in response to the request. A pickup and drop off location of the plurality may be provided to an autonomous vehicle in order to cause the autonomous vehicle to transport a passenger to the pickup or drop off location.