View source for 18377443. DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF simplified abstract (HYUNDAI MOTOR COMPANY)

=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 18377443 titled 'DEEP LEARNING-BASED COLLISION SAFETY CONTROL SYSTEM AND AN OPERATION METHOD THEREOF

==Simplified Explanation==

The patent application 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 based on pre-collision and post-collision data signals.

* The collision safety control system includes a memory storing a collision safety model with deep learning-based collision safety control logic.
* A processor is connected to the memory and is configured to train the collision safety model based on pre-collision and post-collision data signals.
* The collision safety model outputs a collision type and required time-to-fire of passenger protection equipment based on the trained data.

== Potential Applications ==
This technology could be applied in automotive safety systems, such as airbag deployment and seatbelt tensioning, to improve passenger protection in the event of a collision.

== Problems Solved ==
This technology helps in accurately determining the type of collision and the required time for passenger protection equipment to deploy, ensuring optimal safety measures are taken in a timely manner.

== Benefits ==
- Enhanced passenger safety in vehicle collisions
- Efficient deployment of protection equipment
- Utilization of deep learning for advanced collision safety control

== Potential Commercial Applications ==
The technology could be integrated into automotive safety systems by car manufacturers to enhance the safety features of their vehicles, potentially increasing consumer confidence and sales.

== Possible Prior Art ==
Prior art in collision safety control systems may include traditional models that rely on predetermined algorithms rather than deep learning techniques for collision type and passenger protection equipment deployment predictions.

=== Unanswered Questions ===

=== How does this technology impact overall vehicle safety systems? ===
This technology significantly improves the accuracy and efficiency of vehicle safety systems, ultimately enhancing passenger protection in collision scenarios.

=== What are the potential limitations of using deep learning in collision safety control systems? ===
One potential limitation could be the need for extensive training data to ensure the accuracy and reliability of the collision safety model. Additionally, the computational resources required for deep learning algorithms may pose challenges for implementation in certain vehicles.


==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]]