Vehicle Occupant Risk Recognition for Open-Window Extremity Hazards
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Solution Overview
Problem
Existing vehicle safety systems fail to effectively detect and mitigate risks associated with occupants sticking extremities out of car windows, leading to potential accidents, injuries, and fines due to exposure to debris and high winds.
Innovation Solution
A vehicle-based system that uses interior and exterior image sensors to detect and track occupant extremities, assess the risk level based on persistence time and extremity type, and trigger alerts or risk mitigation actions such as deactivating window movement to prevent hazardous behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing vehicle safety systems are used, then basic safety functions are provided, but they fail to detect and mitigate risks associated with occupants sticking extremities out of car windows
Solution Approach 1:
The system dynamically adjusts monitoring based on window state. When the window is detected to be open, the system activates extremity detection; when closed, monitoring is reduced. This dynamic adaptation resolves the contradiction by providing enhanced safety only when necessary, avoiding unnecessary complexity during normal operation.
Solution Approach 2:
The patent introduces an intermediary detection layer between the occupant and the hazard. Image sensors and processing systems act as intermediaries that detect extremity positions and predict collision risks before actual contact occurs, enabling preventive safety interventions without requiring direct contact with the hazard.
2Measurement precision
If image sensors and tracking systems are added to detect extremities, then detection precision is improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into specialized modules: image sensors for capture, extremity detection algorithms for processing, collision risk prediction for analysis, and mitigation control for execution. This segmentation allows each component to be optimized independently, achieving high precision without proportionally increasing overall system complexity.
Solution Approach 2:
The system uses multi-functional image sensors that serve both as primary detection devices and as sources for generating area of interest boundaries. The same sensor data is reused for multiple purposes (extremity detection, window state monitoring, collision prediction), reducing the need for additional specialized components and thereby limiting complexity growth.
3Reliability
If real-time monitoring and risk mitigation actions are implemented, then safety reliability is improved, but response time and processing duration increase
Solution Approach 1:
The system performs preliminary actions by pre-defining area of interest boundaries based on window position and vehicle geometry before actual extremity detection is needed. When extremities are detected, the collision risk can be immediately assessed against pre-computed safety zones, eliminating the need for complex real-time geometric calculations and reducing response time.
Solution Approach 2:
The system uses persistent tracking to skip redundant detection cycles. Once an extremity is detected in a high-risk position, the system maintains continuous tracking through multiple frames, allowing rapid confirmation of the hazard state without re-running full detection algorithms, thus accelerating the response while maintaining accuracy.
Data Source
AI summary
A vehicle including one or more interior image sensors, one or more exterior image sensors, and a controller configured to automatically detect occupant risky behavior is provided. The controller is configured to: detect whether a vehicle occupant exists who may pose a risk; determine whether a detected vehicle occupant in engaged in potentially risky behavior in an area of interest (AoI) based on image data from the one or more interior image sensors or the one or more exterior image sensors; determine whether the potentially risky behavior poses an actual risk; classify a level of risk when the potentially risky behavior poses an actual risk; and perform risk mitigation actions based on risk classification.