View source for Hyundai motor company (20240116501). DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF simplified abstract

=DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF=


==Organization Name==

[[:Category:hyundai motor company|hyundai motor company]]


[[Category:hyundai motor company]]

==Inventor(s)==

[[:Category:Hyung Wook Park of Seoul (KR)|Hyung Wook Park of Seoul (KR)]][[Category:Hyung Wook Park of Seoul (KR)]]

[[:Category:Sung Roh Yoon of Seoul (KR)|Sung Roh Yoon of Seoul (KR)]][[Category:Sung Roh Yoon of Seoul (KR)]]

[[:Category:Dong Hyeok Lee of Seoul (KR)|Dong Hyeok Lee of Seoul (KR)]][[Category:Dong Hyeok Lee of Seoul (KR)]]

==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.



[[Category:B60W30/095]]