Convolutional Neural Network for Vehicle Cognitive State Analysis
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Solution Overview
Problem
Current vehicle manipulation systems fail to effectively utilize occupant cognitive state data to optimize vehicle operations, leading to inefficient travel experiences and potential safety issues due to lack of real-time emotional and mental state analysis.
Innovation Solution
A computer-implemented method using convolutional neural networks for cognitive state analysis, which collects and processes image and audio data from vehicle occupants to infer emotional and mental states, enabling adaptive vehicle manipulation such as route adjustments and feature configurations based on occupant preferences and emotional responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicle manipulation systems use traditional methods without cognitive state analysis, then the system complexity is low, but the travel efficiency and safety are insufficient
Solution Approach 1:
The patent replaces traditional mechanical or rule-based vehicle control systems with a cognitive analysis system that uses image processing and neural networks to detect occupant mental states. The convolutional neural network processes facial images to infer cognitive states, which then automatically adjust vehicle parameters, substituting direct mechanical control with intelligent, perception-based control for improved travel efficiency
Solution Approach 2:
The vehicle system automatically monitors occupant cognitive states through onboard cameras and neural network analysis, then self-adjusts vehicle parameters such as speed, route, and climate control without requiring manual input. The system serves itself by using its own sensors and processors to make real-time operational decisions that optimize travel efficiency based on detected mental states
2Reliability
If real-time cognitive state analysis is implemented, then safety and travel efficiency improve, but the computational resources and processing time increase
Solution Approach 1:
The patent divides the cognitive state analysis into distinct processing stages: image capture by onboard cameras, facial feature extraction through convolutional neural network layers, cognitive state inference by higher-level network layers, and separate vehicle control adjustments. This segmentation allows efficient allocation of computational resources to each stage, reducing overall energy consumption while maintaining real-time safety monitoring
Solution Approach 2:
The system performs preliminary processing of facial images through multiple convolutional layers that pre-extract relevant features before passing them to cognitive state classification layers. This preliminary action prepares data in advance, reducing the computational burden during critical real-time decision-making moments and lowering energy requirements for immediate safety responses
3Measurement precision
If detailed facial image analysis is performed to identify cognitive states, then the precision of mental state detection improves, but the privacy concerns and data processing requirements increase
Solution Approach 1:
The patent extracts only the specific facial features and cognitive indicators needed for mental state detection through the neural network, separating these essential data elements from unnecessary personal information. The system takes out only the relevant biometric data required for cognitive analysis while discarding or protecting other personal identifiers, reducing data processing complexity while maintaining detection precision
Data Source
AI summary
Disclosed embodiments provide for vehicle manipulation using convolutional image processing. The convolutional image processing is accomplished using a computer, where the computer can include a multilayered analysis engine. The multilayered analysis engine can include a convolutional neural network (CNN). The computer is initialized for convolutional processing. A plurality of images is obtained using an imaging device within a first vehicle. A multilayered analysis engine is trained using the plurality of images. The multilayered analysis engine includes multiple layers that include convolutional layers hidden layers. The multilayered analysis engine is used for cognitive state analysis. The evaluating provides a cognitive state analysis. Further images are analyzed using the multilayered analysis engine. The further images include facial image data from one or more persons present in a second vehicle. Voice data is collected to augment the cognitive state analysis. Manipulation data is provided to the second vehicle based on the evaluating.