Vehicle Interaction Point Mapping for Safe Passenger Stops
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Solution Overview
Problem
Autonomous vehicles face challenges in determining safe and convenient interaction points, such as pick-up or drop-off locations, due to the inability to interpret complex environments and obstacles, leading to potential hazards for passengers and vehicles.
Innovation Solution
The system uses sensor data from vehicles, including LiDAR, optical cameras, and OCR, to disambiguate physical structures and identify features like doors, windows, and parking spots, filtering interaction points based on real-time data to prioritize safe and accessible locations for loading and unloading passengers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the vehicle uses basic sensor data for navigation, then the system complexity is low, but the ability to identify safe interaction points and interpret complex environments is insufficient
Solution Approach 1:
The system segments the environment recognition task into multiple processing stages: raw sensor data acquisition, contextual information extraction, candidate interaction point generation, and filtering. This segmentation allows complex environmental interpretation to be broken down into manageable processing steps, improving safety without overwhelming system complexity
Solution Approach 2:
The system performs preliminary action by pre-identifying and mapping contextual information about physical structures (doorways, windows, parking spots, loading zones) before the vehicle arrives at the destination. This advance preparation enables faster, safer decision-making at the actual interaction point without requiring complex real-time analysis
2Measurement precision
If the vehicle filters all candidate interaction points using multiple criteria, then the safety and accuracy of selected points improve, but the processing time and computational load increase
Solution Approach 1:
The system performs preliminary filtering by pre-mapping contextual information and identifying potential interaction points before the vehicle reaches the destination. This advance preparation reduces the computational burden and processing time required when the vehicle actually needs to select an interaction point
Solution Approach 2:
The system applies different filtering criteria with varying strictness to different types of interaction points based on their local characteristics. For example, drop-off points near doorways may use different filtering parameters than parking spots in open areas, optimizing both accuracy and processing efficiency for each specific context
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine contextual information describing at least one physical structure corresponding to a location based at least in part on data captured by one or more sensors of a vehicle. A set of candidate interaction points for the at least one physical structure can be determined based at least in part on the determined contextual information describing the at least one physical structure corresponding to the location. The set of candidate interaction points can be filtered to identify one or more interaction points. An interaction point can be selected from the one or more interaction points to use for stopping the vehicle.