Vehicle Interior Marker Detection for Autonomous Occupancy Response
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Solution Overview
Problem
Autonomous vehicles lack an efficient method to determine their internal state, such as occupancy and passenger conditions, especially in areas without cellular service or when a human operator is not available, leading to potential safety issues and inefficiencies.
Innovation Solution
A system using cameras and machine learning models to process images of the vehicle's interior, identifying visible markers and determining the internal state, which allows the vehicle to respond appropriately, such as providing notifications or controlling actions, without relying on human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If autonomous vehicles use traditional methods to determine internal state, then they can operate with human assistance, but they require cellular service and human operators which reduce efficiency and privacy
Solution Approach 1:
The vehicle performs self-diagnosis of its internal state using onboard cameras and machine learning models, eliminating the need for human operators or external cellular service. The system captures images of the interior, processes them locally to detect occupancy, seatbelt usage, and other internal conditions, and autonomously determines the vehicle's internal state without external assistance.
2Loss of information
If the vehicle processes images locally using machine learning models, then privacy is improved and cellular service is not required, but device complexity increases
Solution Approach 1:
The patent introduces visible markers as intermediaries to simplify the image processing task. These markers are placed on seats and seatbelts, providing clear visual cues that the machine learning model can easily detect. This intermediary element bridges the gap between the complex processing requirements and the need for privacy protection, enabling local processing with reduced computational burden.
3Measurement precision
If the vehicle uses visible markers for internal state detection, then measurement precision is improved, but device complexity increases due to marker installation
Solution Approach 1:
The visible markers utilize color or pattern changes to indicate different internal states. For example, markers on seatbelts change appearance or position based on whether the seatbelt is fastened, and markers on seats indicate occupancy status. This visual encoding method provides high measurement precision for internal state detection while keeping the marker system relatively simple and easy to implement.
Data Source
AI summary
Aspects of the disclosure relate to determining and responding to an internal state of a self-driving vehicle. For instance, an image of an interior of the vehicle captured by a camera mounted in the vehicle is received. The image is processed in order to identify one or more visible markers at predetermined locations within the vehicle. The internal state of the vehicle is determined based on the identified one or more visible markers. A responsive action is identified action using the determined internal state, and the vehicle is controlled in order to perform the responsive action.