Pickup Map Data Using Inconvenience Values for Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face challenges in determining convenient pickup and drop-off locations due to the difficulty in assessing the inconvenience of potential stopping places, leading to inefficiencies in passenger transportation, especially for individuals with disabilities or those in crowded areas.
Innovation Solution
A method that calculates an inconvenience value based on the difference between observed passenger distances and road edge distances, using perception data to assess the convenience of locations and generate map data for autonomous vehicles to identify optimal stopping points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous vehicles use traditional taxi service coordination methods (physical signals, phone calls, in-person discussion), then human drivers can easily coordinate pickup locations, but autonomous vehicles face difficulty in achieving coordination and cause significant inconvenience to passengers
Solution Approach 1:
The patent replaces traditional mechanical coordination methods (physical signals, phone calls, in-person discussions) with an automated electronic system. The server computing device communicates with client computing devices and autonomous vehicles through electronic messages, automatically determining pickup locations and sharing them without human intervention. This substitution eliminates the need for complex human-to-human coordination while improving ease of operation for autonomous vehicles.
2Reliability
If autonomous vehicles stop at locations far from road edges or in crowded areas, then passengers may have difficulty reaching the vehicle or the vehicle may have difficulty finding a stopping place, but this causes significant inconvenience and safety issues especially for passengers with disabilities
Solution Approach 1:
The patent implements a feedback mechanism where the server computing device receives information about pickup location conditions (such as proximity to road edges, crowd levels, and accessibility) and uses this feedback to determine optimal pickup locations. The system evaluates multiple potential locations and selects those that minimize passenger inconvenience and safety risks while ensuring the vehicle can reliably stop. This feedback-driven approach balances vehicle stopping reliability with passenger safety and convenience.
Solution Approach 2:
The patent changes the parameters used for selecting pickup locations from simple proximity-based metrics to comprehensive evaluation criteria including distance to road edges, crowd density, accessibility for disabled passengers, and safety considerations. By adjusting these parameters and their weights, the system can adapt to different scenarios and prioritize passenger safety and convenience over mere stopping convenience for the vehicle.
3Measurement precision
If the system collects and processes detailed perception data about passenger movements and locations, then it can accurately assess inconvenience and generate precise map data, but this increases data processing complexity and computational requirements
Solution Approach 1:
The patent extracts only the essential features and parameters from the collected perception data that are necessary for assessing inconvenience, such as passenger position relative to the vehicle, distance to road edges, and key environmental factors. Rather than processing all raw sensor data, the system identifies and extracts the critical information needed for pickup location determination, reducing computational complexity while maintaining measurement precision.
Solution Approach 2:
The patent segments the data processing task into distinct modules: data collection from sensors, feature extraction and processing, inconvenience calculation, and map data generation. Each module handles specific aspects of the data, allowing for optimized processing pipelines and reducing overall system complexity. The segmentation enables parallel processing and makes the system more manageable and scalable.
Data Source
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AI summary
Aspects of the disclosure relate to generating map data. For instance, data generated by a perception system 372 of a vehicle 101 may be received. This data corresponds to a plurality of observations including observed positions of a passenger of the vehicle as the passenger approached the vehicle at a first location. The data may be used to determine an observed distance traveled by a passenger to reach a vehicle. A road edge distance between an observed position of an observation of the plurality of observations and a nearest road edge to the observed position may be determined. An inconvenience value for the first location may be determined using the observed distance and the road edge distance. The map data is then generated using the inconvenience value.