Hyundai motor company (20240116501). DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF simplified abstract
Contents
- 1 DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF
Organization Name
Inventor(s)
DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240116501 titled 'DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF
Simplified Explanation
The abstract describes a collision safety control system that utilizes a deep learning-based collision safety model to determine collision type and required time-to-fire of passenger protection equipment.
- The system includes a memory storing the collision safety model and a processor connected to the memory.
- The processor is trained based on pre-collision and post-collision data to output collision type and required time-to-fire of passenger protection equipment.
- The collision safety control logic is deep learning-based, allowing for advanced analysis of collision scenarios.
Potential Applications
This technology can be applied in automotive safety systems, such as airbag deployment and seatbelt tensioning, to enhance passenger protection in the event of a collision.
Problems Solved
1. Improved accuracy in determining collision type and required time-to-fire of passenger protection equipment. 2. Enhanced safety measures for passengers in vehicles.
Benefits
1. Increased safety for vehicle occupants. 2. Advanced collision detection and response capabilities. 3. Potential reduction in injuries and fatalities in automotive accidents.
Potential Commercial Applications
"Advanced Collision Safety Control System for Automotive Vehicles" can be used in the automotive industry to develop next-generation safety features for vehicles, attracting safety-conscious consumers.
Possible Prior Art
Prior art may include existing collision safety systems that rely on traditional algorithms rather than deep learning models for collision analysis.
Unanswered Questions
How does this technology compare to existing collision safety systems in terms of accuracy and efficiency?
This article does not provide a direct comparison between this technology and existing collision safety systems. Further research or testing may be needed to determine the superiority of this innovation.
What are the potential limitations or challenges in implementing this deep learning-based collision safety control system in vehicles?
The article does not address any potential limitations or challenges in implementing this technology. Factors such as cost, compatibility with existing systems, and regulatory approval may need to be considered.
Original Abstract Submitted
a collision safety control system includes a memory storing a collision safety model having a deep learning-based collision safety control logic. the collision safety control system also includes a processor electrically connected to the memory. the processor is configured to, in accordance with the collision safety control logic, train, based on at least one signal including pre-collision data and post-collision data, the collision safety model such that the collision safety model outputs a collision type and a required time-to-fire (rttf) of passenger protection equipment corresponding to the at least one signal.
