In-Cabin Activity Detection for Autonomous Vehicle Safety Control
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Solution Overview
Problem
Conventional systems for autonomous vehicles lack comprehensive analysis of in-cabin activities, such as driver posture and hand gestures, limiting their ability to determine safe and comfortable actions, especially in situations requiring human-machine interaction adjustments.
Innovation Solution
A system that uses machine learning models and deep neural networks to accurately identify driver and passenger activities through body position, size, and hand gestures, enabling adaptive human-machine interactions, such as notifications or safety maneuvers, based on real-time in-cabin data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional weight or heat sensors are used to detect driver presence, then basic occupancy detection is achieved, but comprehensive analysis of driver posture, gestures, and activities cannot be performed
Solution Approach 1:
The patent replaces conventional mechanical sensors (weight sensors, heat sensors) with a vision-based system using cameras and deep neural networks. This substitution enables comprehensive analysis of driver posture, gestures, and activities through image processing and machine learning, achieving high measurement precision without the limitations of traditional sensor approaches.
Solution Approach 2:
The vision-based system serves multiple functions simultaneously: it detects driver presence, analyzes posture, identifies hand gestures, recognizes activities (texting, reading, etc.), and determines safety conditions. This multi-functional approach replaces multiple dedicated sensors with a single comprehensive visual analysis system.
2Measurement precision
If raw images are used to identify passenger location, then basic presence detection is achieved, but detailed analysis of driver actions, postures, and gestures cannot be performed
Solution Approach 1:
The patent replaces simple image-based presence detection with an advanced deep learning system that processes visual data through trained neural networks. This enables precise detection of subtle driver states including posture, hand gestures, and specific activities, overcoming the limitations of basic image analysis.
Solution Approach 2:
The system transforms raw image data into meaningful driver state parameters through deep neural network processing. By changing the parameter representation from simple pixel values to extracted features (posture angles, gesture classifications, activity labels), the system achieves accurate detection of complex driver behaviors.
3Adaptability or versatility
If basic occupancy detection is performed without granular activity analysis, then system simplicity is maintained, but adaptive human-machine interactions cannot be properly adjusted
Solution Approach 1:
The system continuously monitors driver activities and provides feedback to the human-machine interaction system. By detecting driver state (posture, gestures, activities) and adjusting HMI accordingly, the system creates a closed-loop adaptive control mechanism that enhances versatility while managing complexity through intelligent processing.
Solution Approach 2:
The deep neural networks are pre-trained with extensive driver behavior data before deployment. This preliminary training enables the system to automatically recognize and adapt to various driver activities without requiring complex real-time decision logic, thereby achieving high adaptability with manageable operational complexity.
Data Source
AI summary
In various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. In particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (DNNs). Using the DNNs, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. These determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.