Pickup Access Point Inference Using Curated Map Features
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying suitable pickup or drop-off locations due to the lack of detailed information about buildings and access points in existing digital maps, leading to inefficient location selection and increased user travel time.
Innovation Solution
The system utilizes detailed electronic map information, combining roadgraph data with other features to infer appropriate pickup and drop-off locations by filtering and selecting access points based on curated features, considering distance, walking time, and wait times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed electronic map information is used to infer access points, then pickup and drop-off location accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing map data to extract and store access point information, building features, and land parcel boundaries before actual pickup/drop-off requests. This pre-computation reduces real-time processing complexity while maintaining high location accuracy.
Solution Approach 2:
The system introduces an intermediary layer of curated features that act as mediators between raw map data and access point inference. These features include pre-identified building entrances, parking areas, and walkable paths, which simplify the complex task of inferring access points from detailed electronic map information.
2Measurement precision
If multiple curated features are considered for access point inference, then location selection accuracy is improved, but computational time increases
Solution Approach 1:
The system applies local quality by filtering and prioritizing curated features based on their relevance to the specific pickup or drop-off location. Instead of uniformly processing all features, the system identifies and focuses on locally relevant features such as nearby building entrances, parking areas, or walkable paths, reducing computational time while maintaining accuracy.
Solution Approach 2:
The system changes parameters by dynamically adjusting the selection criteria for curated features based on the specific context of each request. This includes modifying distance thresholds, priority weights, and feature types considered, allowing the system to balance accuracy and computational time efficiently for different scenarios.
Data Source
AI summary
Aspects of the technology provide a method including receiving a request for a user to be picked up or dropped off by an autonomous vehicle, in which the request identifying a location, and determining a land parcel containing the identified location. The method also includes identifying a set of features that are within a selected distance from the identified location, filtering the set of identified features to obtain only curated features that are within the selected distance from the identified location, determining a distance between each curated feature and the identified location, and inferring an access point for the identified location based on the distances determined between each curated feature and the identified location. The inferred access point can then be provided to enable the autonomous vehicle to perform a pickup or drop-off.