Autonomous Vehicle Curb-Height Stop Selection for Low-Mobility Access
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Solution Overview
Problem
Autonomous vehicles face challenges in identifying optimal pick-up and drop-off locations, as current technologies rely on human judgment to navigate nuanced decisions, such as avoiding obstacles like puddles, which autonomous vehicles equipped with sensor systems can also utilize for more efficient passenger loading and unloading.
Innovation Solution
The autonomous vehicle system uses data on curb heights, combined with passenger user profiles, to determine the best stopping location by analyzing sensor data and communicating with a remote computing system to adjust its navigation for easier passenger access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous vehicles use sensor systems to detect obstacles like puddles, then navigation safety is improved, but the complexity of determining optimal stopping locations increases
Solution Approach 1:
The system performs preliminary detection of curb heights and obstacle locations using sensor systems before finalizing the stopping location. By gathering this information in advance and comparing it against passenger mobility needs, the system resolves the contradiction by preparing navigation data beforehand, thus maintaining safety while streamlining the decision-making process.
2Ease of operation
If the vehicle adjusts stopping location based on curb height data, then passenger access ease is improved, but the time required to determine optimal location increases
Solution Approach 1:
The system uses feedback from curb height sensor data and passenger profile information to automatically adjust stopping locations. The computing system receives real-time data about curb heights at potential stopping points and compares this with the passenger's mobility characteristics, enabling quick automated decisions that improve access ease without significant time loss.
3Measurement precision
If the system considers multiple factors including curb height and passenger profile, then decision accuracy is improved, but computational complexity increases
Solution Approach 1:
The system changes parameters by focusing on specific measurable factors such as curb height values and passenger mobility indicators from profiles. By translating complex environmental and passenger data into standardized parameters that can be directly compared against predefined criteria, the system achieves high decision accuracy while managing computational complexity through parameter standardization.
Data Source
AI summary
The present technology is effective to cause at least one processor to receive attributes of a sidewalk section within a threshold distance from a location selected by a passenger, determine a potential location for pick-up or drop-off of the passenger by the autonomous vehicle, receive an authorization from the passenger, and navigate the autonomous vehicle to the potential location for pick-up or drop-off. The attributes may include a respective curb height of the sidewalk section within the threshold distance. The potential location may be determined based upon a height of a portion of an autonomous vehicle and the respective curb height of the sidewalk section within the threshold distance. The authorization may confirm the potential location for pick-up or drop-off.