3D CNN Emergency Data Detection for Vehicles
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Solution Overview
Problem
Current navigation systems fail to accurately and timely notify vehicles of potential hazardous situations, which is critical for both human-driven and autonomous vehicles, as they often rely on outdated or incomplete map data.
Innovation Solution
A system utilizing a 3D convolution neural network (CNN) processes live video data from vehicle-mounted cameras to detect emergency situations by extracting spatial features and generating real-time notifications, enabling accurate and timely alerts for hazards such as accidents, natural disasters, or obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional navigation systems use outdated map data, then system complexity is reduced, but measurement precision of hazardous situations deteriorates
Solution Approach 1:
The patent replaces traditional mechanical/map-based navigation systems with a deep learning-based computer vision system. The 3D-CNN model processes live video feeds from vehicle-mounted cameras to detect hazardous situations, substituting outdated map data with real-time visual analysis. This achieves high measurement precision for hazard detection while maintaining manageable system complexity through software-based processing.
Solution Approach 2:
The patent introduces live video data as an intermediary between the vehicle and hazard detection. Instead of directly querying map data, the system uses camera feeds as an intermediate source of real-time environmental information. The 3D-CNN model processes these video intermediaries to extract hazard information, enabling accurate detection without relying on complex or outdated map structures.
2Reliability
If real-time video processing is implemented, then reliability of emergency notification is improved, but use of energy increases
Solution Approach 1:
The patent applies partial action by processing only the most critical frames or regions of video data through the computationally intensive 3D-CNN model. Rather than analyzing every pixel in every frame, the system selectively processes key moments or areas where hazards are most likely to occur. This maintains high reliability for emergency detection while reducing overall energy consumption compared to continuous full-frame processing.
3Measurement precision
If 3D-CNN model with multiple layers is used, then measurement precision of emergency data is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex 3D-CNN model into distinct functional layers: convolution layers for feature extraction, pooling layers for dimensionality reduction, and fully connected layers for classification. This segmentation allows the system to achieve high measurement precision through multiple processing stages while managing complexity by organizing functions into modular, specialized components that can be independently optimized and trained.
Data Source
AI summary
A system, a method, and a computer program product are provided for determining emergency data in a region. The system may include a processor configured to execute computer program code instructions stored in a memory to obtain live video data associated with the vehicle in the region and determine the emergency data of the vehicle from the live video data using a three-dimensional convolution neural network (3D-CNN) model. The live video data may include one or more video clips. The 3D-CNN model may include a plurality of convolution layers, a plurality of pooling layers, and a plurality of fully connected layers. The processor is further configured to generate an emergency notification based on the emergency data and providing the emergency notification to one or more subjects.